diff --git a/.gitignore b/.gitignore index c82695f..c729426 100644 --- a/.gitignore +++ b/.gitignore @@ -3,7 +3,7 @@ __pycache__/ .DS_Store # Generated topic workspace -/AGENTS.md +/TOPIC_AGENTS.md /agents/ /topic.json /topic-dashboard.html diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..392556d --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,47 @@ +# Topic Scout Repository Guide + +## Purpose + +This repository is a reusable Topic Scout tool. It is meant to help agents and human operators +create topic-specific research workspaces for literature scouting, paper triage, corpus +maintenance, dashboard generation, and opportunity analysis. + +Use this repository when the goal is to: + +- turn a raw research intent into a structured topic contract +- discover papers with OpenAlex and citation-graph expansion +- rank candidate papers with an LLM +- build and maintain an accepted paper corpus +- generate a research dashboard and evidence-backed opportunities +- emit task manifests for sequential, Claw-style, or swarm execution + +## Command Surface + +Primary commands: + +1. `make init` +2. `make scout` +3. `make review` +4. `make corpus` +5. `make opportunities` +6. `make dashboard` +7. `python3 scripts/orchestrate.py emit --mode claw` +8. `python3 scripts/orchestrate.py emit --mode swarm` + +## Repo Rules + +- Treat the repository root as the live generated workspace surface. +- Do not assume the current topic; inspect `topic.json` and `TOPIC_AGENTS.md` after `make init`. +- `AGENTS.md` is permanent repo-level guidance. +- `TOPIC_AGENTS.md` is generated topic-specific guidance. +- Generated role briefs live under `agents/`. +- Generated topic-specific skills live under `skills/topic-paper-scout/` and + `skills/analyze-research-gaps/`. +- Example outputs live under `examples/` and should not be treated as the active workspace. + +## Validation + +- Do not stop at implementation alone. +- Run the relevant commands yourself. +- Verify generated outputs are structurally correct and substantively usable before claiming + success. diff --git a/Makefile b/Makefile index 96061fa..25a3cf0 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,7 @@ PYTHON ?= python3 QUERY ?= -.PHONY: help init reset scout review corpus dashboard plan opportunities-check test +.PHONY: help init reset scout review corpus opportunities dashboard plan opportunities-check test help: @printf "%s\n" \ @@ -10,6 +10,7 @@ help: " make scout Search scholarly graphs for candidate papers" \ " make review Print the candidate review queue" \ " make corpus Rebuild Markdown paper notes and report" \ + " make opportunities Generate LLM-backed research opportunities" \ " make dashboard Build HTML dashboard, graph, wiki, and opportunities" \ " make plan Print the generated multi-agent task plan" \ " make opportunities-check Validate LLM opportunity JSON" \ @@ -19,7 +20,7 @@ init: $(PYTHON) scripts/init_topic.py reset: - rm -rf AGENTS.md agents data/candidates.json data/dashboard.json \ + rm -rf TOPIC_AGENTS.md agents data/candidates.json data/dashboard.json \ data/papers.json data/research_opportunities.json \ data/sequential_tasks.json data/claw_tasks.json data/swarm_tasks.json \ reports scout_cron_payload.txt skills/analyze-research-gaps \ @@ -35,8 +36,12 @@ corpus: $(PYTHON) scripts/build_corpus.py dashboard: + $(PYTHON) scripts/analyze_research_gaps.py $(PYTHON) scripts/build_dashboard.py +opportunities: + $(PYTHON) scripts/analyze_research_gaps.py + plan: $(PYTHON) scripts/orchestrate.py plan diff --git a/README.md b/README.md index 4b7ed7b..88965d9 100644 --- a/README.md +++ b/README.md @@ -1,163 +1,167 @@ -# Topic Scouting and Auto Dashboard Generation - -[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.21009802.svg)](https://doi.org/10.5281/zenodo.21009802) - -Turn any research question into a persistent, agent-operated paper intelligence workspace. - -AI Topic Scout asks the user what they want to investigate, generates topic-specific agent and -skill instructions, searches scholarly graphs, maintains a deduplicated paper corpus, and builds: - -- Markdown paper notes and synthesis reports -- topic ratios and scout-run trends -- an interactive paper association graph -- a searchable Karpathy-style wiki -- evidence-backed research gaps and opportunities -- task manifests for one agent, Claw agents, or subagent swarms - -## Quick Start - -```bash -codex login -make init -make scout -make review -make dashboard +# AI Topic Scout — Automated Literature Review & Paper Discovery for AI Agents + +[![GitHub stars](https://img.shields.io/github/stars/ginaecho/topic-scout?style=flat-square)](https://github.com/ginaecho/topic-scout/stargazers) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=flat-square)](https://opensource.org/licenses/MIT) +[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg?style=flat-square)](https://www.python.org/) +[![OpenAlex](https://img.shields.io/badge/data-OpenAlex-green.svg?style=flat-square)](https://openalex.org/) + +> **The hard part of research is not finding papers — it's maintaining a living corpus that keeps up with a field.** + +**AI Topic Scout** is an open-source multi-agent tool that turns a plain-language research intent into a self-updating literature review workspace. It integrates [OpenAlex](https://openalex.org/) scholarly graph search, LLM-backed relevance ranking, citation-graph expansion, and multi-agent task emission — so you can build and maintain a living paper corpus for any AI research topic without manual searching. + +--- + +## ⚡ TL;DR + +Describe a research topic in natural language → `make init` generates a workspace → `make scout` queries OpenAlex, ranks candidates with an LLM, and auto-accepts papers above a relevance threshold → `make dashboard` produces an interactive HTML dashboard with a citation graph, research wiki, trends, cost tracking, and gap analysis. Emit the full workflow as Claw or swarm task manifests for multi-agent orchestration. One full run costs cents of LLM API. + +--- + +## 🏗️ Architecture + +```mermaid +flowchart TB + subgraph Input + A["Research Intent\n(natural language)"] + end + subgraph Discovery + B["OpenAlex API\nscholarly graph search"] + C["Citation Graph\nneighborhood expansion"] + end + subgraph Ranking + D["LLM Scorer\nrelevance ranking & triage"] + end + subgraph Corpus + E["papers.json\naccepted corpus"] + F["Markdown Notes\nper paper"] + G["Synthesis Report"] + end + subgraph Orchestration + H["Sequential Tasks"] + I["Claw Manifests"] + J["Swarm Manifests"] + end + subgraph Output + K["Dashboard HTML\ngraph · wiki · trends · costs"] + L["Research Gaps\nopportunity analysis"] + end + A --> B + B --> C + C --> D + D --> E + E --> F + E --> G + E --> H & I & J + G --> K & L ``` -By default, `make init` calls the installed Codex CLI in non-interactive mode and reuses its saved -ChatGPT login. This uses Codex subscription access and does not require an OpenAI API key. The -Codex agent runs ephemerally in a read-only sandbox and returns schema-constrained JSON. - -During intake, it converts the user's raw intent into a concise title, research question, dashboard -structure, targeted search queries, and a topic-specific scouting strategy before generating any -agent or skill Markdown. +--- -Or initialize non-interactively: +## 🚀 Quick Start ```bash -python3 scripts/init_topic.py \ - --intent "Evaluate AI agents for theorem proving, including proof correctness, verifier feedback, and practical research-engineering value" \ - --goal "Track methods that improve formal proof search and verification" \ - --audience "research engineers" \ - --include "formal theorem proving, proof search, verifier-guided generation" \ - --exclude "informal math tutoring" \ - --years "2023-2026" \ - --taxonomy "proof generation,proof search,verification,benchmarks,systems" +codex login +make init # refine intent → generate topic workspace +make scout # OpenAlex search + LLM ranking +make corpus # build paper notes + synthesis report +make opportunities # generate research gaps +make dashboard # interactive HTML dashboard ``` -Provider options: - -```bash -# Default: use the logged-in Codex CLI and ChatGPT/Codex subscription -make init - -# Direct Responses API usage and API billing -export OPENAI_API_KEY="..." -python3 scripts/init_topic.py --provider api - -# No model call -python3 scripts/init_topic.py --offline +**End-to-end pipeline:** + +```mermaid +flowchart LR + A["natural-language\nintent"] -->|make init| B["topic.json\nworkspace"] + B -->|make scout| C["ranked\ncandidates"] + C --> D{approval\nrequired?} + D -->|yes| E["make review\n+ accept"] + D -->|"no — auto-accept\n≥ score"| F["papers.json\ncorpus"] + E --> F + F -->|make corpus| G["notes +\nreport"] + G -->|make opportunities| H["research\ngaps"] + H -->|make dashboard| I["topic-dashboard\n.html"] ``` -Set `TOPIC_SCOUT_PROVIDER=api` to change the default provider. Pass `--model` to override the -selected provider's configured model. Offline mode preserves literal-input behavior. - -To discard a generated topic workspace and restart intake: +**Provider options:** ```bash -make reset -make init +make init # Codex CLI — no API key needed +export OPENAI_API_KEY="..." && python3 scripts/init_topic.py --provider api +python3 scripts/init_topic.py --offline # no LLM call during setup +python3 scripts/scout.py --accept-score 8.0 # custom acceptance threshold +python3 scripts/scout.py --offline # OpenAlex only, zero tokens ``` -`make reset` removes only generated topic files, agent roles, topic-specific skills, corpus state, -scout candidates, reports, task manifests, and dashboard output. These generated paths are also -excluded by `.gitignore`, while application source, examples, schemas, and the setup skill remain -trackable. - -Then inspect: +--- -- `topic.json`: the research contract -- `AGENTS.md`: generated operating instructions -- `skills/topic-paper-scout/SKILL.md`: generated scout procedure -- `skills/analyze-research-gaps/SKILL.md`: generated opportunity-analysis procedure -- `data/papers.json`: normalized scholarly corpus -- `reports/research_report.md`: generated synthesis -- `topic-dashboard.html`: dashboard, graph, wiki, and opportunities -- `scout_cron_payload.txt`: generated prompt for a scheduled Claw scout +## 📦 Outputs -## Intent Intake +| Artifact | Description | +|---|---| +| `data/candidates.json` | Discovered and LLM-ranked candidate papers | +| `data/papers.json` | Accepted corpus + full scout history | +| `reports/research_report.md` | Synthesized report over accepted papers | +| `data/research_opportunities.json` | LLM-generated research gaps | +| `topic-dashboard.html` | Interactive dashboard: graph · wiki · trends · costs | +| `data/claw_tasks.json` | Claw-oriented task manifest | +| `data/swarm_tasks.json` | Swarm-oriented task manifest | -`make init` asks for one complete research intent. Immediately after that input, the LLM derives: +`make reset` removes only generated workspace artifacts; source, schemas, and tracked examples remain. -- a concise title and research question; -- business purpose, audience, and scope boundaries; -- evaluation dimensions, evidence types, and taxonomy; -- dashboard sections; -- targeted and adversarial search queries; -- a citation-graph scouting strategy. +--- -Interactive setup then asks only for publication years, cadence, and approval policy. Non-interactive -flags such as `--goal`, `--include`, and `--taxonomy` are optional constraints on the LLM. +## 🤖 Multi-Agent Orchestration (Claw & Swarm) -The refined contract and original `raw_intent` become version-controlled instructions rather than -disappearing into chat history. - -## Agent Modes +```mermaid +flowchart LR + A["orchestrate.py"] -->|sequential| B["Local runner\nstep-by-step"] + A -->|claw| C["claw_tasks.json\nClaw agents"] + A -->|swarm| D["swarm_tasks.json\nSubagent swarm"] + C --> E["Agent: scout"] & F["Agent: review"] & G["Agent: dashboard"] + D --> H["Worker A"] & I["Worker B"] & J["Worker C"] +``` ```bash -# Show the generated task plan -python3 scripts/orchestrate.py plan - -# Run deterministic local stages +python3 scripts/orchestrate.py plan # show the generated task plan python3 scripts/orchestrate.py run --mode sequential - -# Emit task briefs for a Claw coordinator and workers python3 scripts/orchestrate.py emit --mode claw - -# Emit independent subagent tasks and a synthesis task python3 scripts/orchestrate.py emit --mode swarm ``` -Roles: +--- -- `coordinator`: owns the research contract and final acceptance -- `query_designer`: expands the topic into targeted searches -- `graph_scout`: searches scholarly graphs and citation neighborhoods -- `relevance_reviewer`: applies inclusion/exclusion criteria -- `librarian`: deduplicates and writes paper records -- `analyst`: maintains taxonomy, trends, and synthesis -- `gap_analyst`: produces evidence-backed opportunities -- `publisher`: regenerates Markdown and HTML artifacts +## 🛠️ Tool Surface for AI Agents -## Scout Semantics +This repo is structured so coding agents and research agents can treat it as a **tool surface**, not just source code. -- Candidate discovery does not automatically imply acceptance. -- Every accepted paper must have a stable scholarly identifier and source URL. -- Existing records are compared before writeback. -- If no accepted new paper exists, tracked artifacts are not rewritten. -- Research-gap conclusions are LLM hypotheses over the current corpus and must include evidence, - uncertainty, and targeted follow-up queries. +| Command | Action | +|---|---| +| `make init` | Create topic workspace from natural-language intent | +| `make scout` | Discover and rank candidate papers via OpenAlex + LLM | +| `make corpus` | Rebuild paper notes and synthesis report | +| `make opportunities` | Generate evidence-backed research gap analysis | +| `make dashboard` | Regenerate interactive HTML dashboard | +| `make reset` | Clear generated workspace for a fresh topic | -Review and accept candidates: +Review flow (approval-gated): ```bash make review python3 scripts/accept_candidates.py openalex:W123 openalex:W456 -make corpus -make dashboard +make corpus && make opportunities && make dashboard ``` -## Commands +Example outputs live under `examples/ai-in-hiring-processes/` with a complete workspace, accepted corpus, reports, dashboard artifacts, and task manifests. -```bash -make help -make init -make reset -make scout -make corpus -make dashboard -make plan -make test -``` +--- + +## 🧬 Tech Stack & Indexing Keywords + +- **Data source:** OpenAlex scholarly graph API, citation-neighborhood expansion +- **Ranking:** LLM-backed relevance scoring (Codex CLI or OpenAI Responses API) +- **Orchestration:** multi-agent task manifests for Claw and swarm execution +- **Output:** Markdown notes, synthesis report, interactive HTML dashboard, JSON manifests -No Python package installation is required for the core workflow. +**This repo is designed to match searches for:** +`automated literature review` · `AI paper discovery` · `OpenAlex Python` · `citation graph exploration` · `research scouting workflow` · `multi-agent research automation` · `LLM paper ranking` · `research gap analysis` · `living corpus maintenance` · `paper triage tool` · `Claw task manifest` · `swarm agent research` · `research monitoring automation` · `academic paper search agent` diff --git a/examples/ai-in-hiring-processes/AGENTS.md b/examples/ai-in-hiring-processes/AGENTS.md new file mode 100644 index 0000000..2968da3 --- /dev/null +++ b/examples/ai-in-hiring-processes/AGENTS.md @@ -0,0 +1,39 @@ +# AGENTS.md + +## Mission + +Maintain a living research intelligence workspace for **AI in Hiring Processes**. + +Research question: How does AI affect the design, execution, outcomes, and governance of hiring processes across sourcing, screening, assessment, interviewing, selection, and candidate communication? +Goal: Identify where AI can create measurable hiring value, where it introduces operational or legal risk, and which evidence supports responsible adoption and evaluation. +Audience: Talent acquisition leaders, HR operations teams, hiring managers, AI system designers, legal and compliance teams, and organizational decision-makers. + +## Research Contract + +- Include: AI applications across the end-to-end hiring lifecycle, Decision-support, automation, and human-in-the-loop hiring systems, Impacts on hiring speed, cost, quality, consistency, and candidate experience, Validity, reliability, fairness, bias, accessibility, privacy, transparency, and explainability, Performance differences across roles, industries, applicant groups, and labor-market contexts, Data, workflow, monitoring, escalation, audit, and accountability requirements, Applicable employment, anti-discrimination, data-protection, and automated-decision constraints, Failure modes including proxy discrimination, feedback loops, gaming, automation bias, and model drift +- Exclude: AI impacts on general workforce management after hiring, Employee productivity, retention, or performance-management systems unless directly used to validate hiring outcomes, Unsupported vendor marketing claims, Fully autonomous hiring recommendations without evaluation of human oversight and governance, Claims about legal compliance that are not tied to a defined jurisdiction +- Publication window: 2023-2026 +- Evidence types: methods, benchmarks, systems, surveys +- Taxonomy: Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication, AI function: generation, prediction, ranking, classification, recommendation, conversation, workflow automation, Decision role: assistive, advisory, semi-automated, autonomous, Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security, Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor, Risk mechanism: biased data, proxy variables, measurement error, distribution shift, gaming, hallucination, automation bias, weak oversight, Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance +- Dashboard sections: AI use cases by hiring stage and decision role, Business-value metrics: time-to-hire, cost-per-hire, recruiter workload, conversion, and quality-of-hire, Evidence quality and benchmark comparability, Predictive validity, reliability, and generalization across jobs and populations, Fairness outcomes and adverse-impact analysis by applicant group, Candidate experience, accessibility, transparency, and contestability, Human-AI workflow design, override behavior, and accountability, Legal and policy requirements by jurisdiction, Privacy, security, data provenance, and vendor-risk controls, Adversarial risks, failure modes, monitoring signals, and mitigations +- Human approval required: false + +## Roles + +1. `coordinator`: enforce this contract and accept final changes. +2. `query_designer`: maintain targeted and adversarial search queries. +3. `graph_scout`: expand seeds through references, related works, and citing works. +4. `relevance_reviewer`: reject adjacent but out-of-scope work. +5. `librarian`: deduplicate by arXiv/DOI/OpenAlex ID and preserve provenance. +6. `analyst`: synthesize themes, trends, and disagreements. +7. `gap_analyst`: identify bounded, evidence-backed missing areas. +8. `publisher`: regenerate Markdown and HTML only after accepted changes. + +## Hard Rules + +- Candidate discovery is not acceptance. +- Never invent metadata, abstracts, URLs, citations, or conclusions. +- Distinguish corpus gaps from field-wide research gaps. +- If no accepted new paper exists, do not rewrite tracked artifacts. +- Every paper must retain a source URL and stable identifier. +- Every opportunity must state evidence, inference, uncertainty, and future queries. diff --git a/examples/ai-in-hiring-processes/README.md b/examples/ai-in-hiring-processes/README.md new file mode 100644 index 0000000..ad2e49a --- /dev/null +++ b/examples/ai-in-hiring-processes/README.md @@ -0,0 +1,29 @@ +# AI in Hiring Processes + +This directory is a tracked example output from AI Topic Scout. + +It shows what a generated topic workspace looks like after: + +- `make init` +- `make scout` +- `make corpus` +- `make opportunities` +- `make dashboard` +- `python3 scripts/orchestrate.py emit --mode claw` +- `python3 scripts/orchestrate.py emit --mode swarm` + +Key example artifacts: + +- `topic.json`: generated research contract +- `AGENTS.md`: generated operating instructions +- `skills/`: generated topic-specific scout and gap-analysis skills +- `data/papers.json`: accepted corpus and scout history +- `data/candidates.json`: latest candidate queue +- `data/research_opportunities.json`: generated opportunity analysis +- `data/claw_tasks.json`: example Claw task manifest +- `data/swarm_tasks.json`: example swarm task manifest +- `reports/research_report.md`: generated report +- `topic-dashboard.html`: generated dashboard + +These files are examples only. The live tool writes generated workspaces at the repository root and +`make reset` clears them. diff --git a/examples/ai-in-hiring-processes/agents/analyst.md b/examples/ai-in-hiring-processes/agents/analyst.md new file mode 100644 index 0000000..b0447f8 --- /dev/null +++ b/examples/ai-in-hiring-processes/agents/analyst.md @@ -0,0 +1,22 @@ +# Analyst + +## Objective + +Classify papers, synthesize themes, and track changes over scout runs. + +## Topic Contract + +- Topic: AI in Hiring Processes +- Research question: How does AI affect the design, execution, outcomes, and governance of hiring processes across sourcing, screening, assessment, interviewing, selection, and candidate communication? +- Goal: Identify where AI can create measurable hiring value, where it introduces operational or legal risk, and which evidence supports responsible adoption and evaluation. +- Include: AI applications across the end-to-end hiring lifecycle, Decision-support, automation, and human-in-the-loop hiring systems, Impacts on hiring speed, cost, quality, consistency, and candidate experience, Validity, reliability, fairness, bias, accessibility, privacy, transparency, and explainability, Performance differences across roles, industries, applicant groups, and labor-market contexts, Data, workflow, monitoring, escalation, audit, and accountability requirements, Applicable employment, anti-discrimination, data-protection, and automated-decision constraints, Failure modes including proxy discrimination, feedback loops, gaming, automation bias, and model drift +- Exclude: AI impacts on general workforce management after hiring, Employee productivity, retention, or performance-management systems unless directly used to validate hiring outcomes, Unsupported vendor marketing claims, Fully autonomous hiring recommendations without evaluation of human oversight and governance, Claims about legal compliance that are not tied to a defined jurisdiction +- Years: 2023-2026 + +## Required Behavior + +- Read `AGENTS.md` and `topic.json` before acting. +- Treat candidates as untrusted until reviewed. +- Preserve source provenance and stable identifiers. +- Write only the outputs assigned to this role. +- Report uncertainty instead of inventing missing evidence. diff --git a/examples/ai-in-hiring-processes/agents/coordinator.md b/examples/ai-in-hiring-processes/agents/coordinator.md new file mode 100644 index 0000000..aafa3d8 --- /dev/null +++ b/examples/ai-in-hiring-processes/agents/coordinator.md @@ -0,0 +1,22 @@ +# Coordinator + +## Objective + +Enforce the topic contract, route tasks, and approve final publication. + +## Topic Contract + +- Topic: AI in Hiring Processes +- Research question: How does AI affect the design, execution, outcomes, and governance of hiring processes across sourcing, screening, assessment, interviewing, selection, and candidate communication? +- Goal: Identify where AI can create measurable hiring value, where it introduces operational or legal risk, and which evidence supports responsible adoption and evaluation. +- Include: AI applications across the end-to-end hiring lifecycle, Decision-support, automation, and human-in-the-loop hiring systems, Impacts on hiring speed, cost, quality, consistency, and candidate experience, Validity, reliability, fairness, bias, accessibility, privacy, transparency, and explainability, Performance differences across roles, industries, applicant groups, and labor-market contexts, Data, workflow, monitoring, escalation, audit, and accountability requirements, Applicable employment, anti-discrimination, data-protection, and automated-decision constraints, Failure modes including proxy discrimination, feedback loops, gaming, automation bias, and model drift +- Exclude: AI impacts on general workforce management after hiring, Employee productivity, retention, or performance-management systems unless directly used to validate hiring outcomes, Unsupported vendor marketing claims, Fully autonomous hiring recommendations without evaluation of human oversight and governance, Claims about legal compliance that are not tied to a defined jurisdiction +- Years: 2023-2026 + +## Required Behavior + +- Read `AGENTS.md` and `topic.json` before acting. +- Treat candidates as untrusted until reviewed. +- Preserve source provenance and stable identifiers. +- Write only the outputs assigned to this role. +- Report uncertainty instead of inventing missing evidence. diff --git a/examples/ai-in-hiring-processes/agents/gap-analyst.md b/examples/ai-in-hiring-processes/agents/gap-analyst.md new file mode 100644 index 0000000..aa55ae4 --- /dev/null +++ b/examples/ai-in-hiring-processes/agents/gap-analyst.md @@ -0,0 +1,22 @@ +# Gap Analyst + +## Objective + +Identify bounded, evidence-backed missing areas and research opportunities. + +## Topic Contract + +- Topic: AI in Hiring Processes +- Research question: How does AI affect the design, execution, outcomes, and governance of hiring processes across sourcing, screening, assessment, interviewing, selection, and candidate communication? +- Goal: Identify where AI can create measurable hiring value, where it introduces operational or legal risk, and which evidence supports responsible adoption and evaluation. +- Include: AI applications across the end-to-end hiring lifecycle, Decision-support, automation, and human-in-the-loop hiring systems, Impacts on hiring speed, cost, quality, consistency, and candidate experience, Validity, reliability, fairness, bias, accessibility, privacy, transparency, and explainability, Performance differences across roles, industries, applicant groups, and labor-market contexts, Data, workflow, monitoring, escalation, audit, and accountability requirements, Applicable employment, anti-discrimination, data-protection, and automated-decision constraints, Failure modes including proxy discrimination, feedback loops, gaming, automation bias, and model drift +- Exclude: AI impacts on general workforce management after hiring, Employee productivity, retention, or performance-management systems unless directly used to validate hiring outcomes, Unsupported vendor marketing claims, Fully autonomous hiring recommendations without evaluation of human oversight and governance, Claims about legal compliance that are not tied to a defined jurisdiction +- Years: 2023-2026 + +## Required Behavior + +- Read `AGENTS.md` and `topic.json` before acting. +- Treat candidates as untrusted until reviewed. +- Preserve source provenance and stable identifiers. +- Write only the outputs assigned to this role. +- Report uncertainty instead of inventing missing evidence. diff --git a/examples/ai-in-hiring-processes/agents/graph-scout.md b/examples/ai-in-hiring-processes/agents/graph-scout.md new file mode 100644 index 0000000..a0096a4 --- /dev/null +++ b/examples/ai-in-hiring-processes/agents/graph-scout.md @@ -0,0 +1,22 @@ +# Graph Scout + +## Objective + +Expand queries through scholarly references, related works, and citations. + +## Topic Contract + +- Topic: AI in Hiring Processes +- Research question: How does AI affect the design, execution, outcomes, and governance of hiring processes across sourcing, screening, assessment, interviewing, selection, and candidate communication? +- Goal: Identify where AI can create measurable hiring value, where it introduces operational or legal risk, and which evidence supports responsible adoption and evaluation. +- Include: AI applications across the end-to-end hiring lifecycle, Decision-support, automation, and human-in-the-loop hiring systems, Impacts on hiring speed, cost, quality, consistency, and candidate experience, Validity, reliability, fairness, bias, accessibility, privacy, transparency, and explainability, Performance differences across roles, industries, applicant groups, and labor-market contexts, Data, workflow, monitoring, escalation, audit, and accountability requirements, Applicable employment, anti-discrimination, data-protection, and automated-decision constraints, Failure modes including proxy discrimination, feedback loops, gaming, automation bias, and model drift +- Exclude: AI impacts on general workforce management after hiring, Employee productivity, retention, or performance-management systems unless directly used to validate hiring outcomes, Unsupported vendor marketing claims, Fully autonomous hiring recommendations without evaluation of human oversight and governance, Claims about legal compliance that are not tied to a defined jurisdiction +- Years: 2023-2026 + +## Required Behavior + +- Read `AGENTS.md` and `topic.json` before acting. +- Treat candidates as untrusted until reviewed. +- Preserve source provenance and stable identifiers. +- Write only the outputs assigned to this role. +- Report uncertainty instead of inventing missing evidence. diff --git a/examples/ai-in-hiring-processes/agents/librarian.md b/examples/ai-in-hiring-processes/agents/librarian.md new file mode 100644 index 0000000..dc21e16 --- /dev/null +++ b/examples/ai-in-hiring-processes/agents/librarian.md @@ -0,0 +1,22 @@ +# Librarian + +## Objective + +Deduplicate accepted papers and preserve identifiers, URLs, and provenance. + +## Topic Contract + +- Topic: AI in Hiring Processes +- Research question: How does AI affect the design, execution, outcomes, and governance of hiring processes across sourcing, screening, assessment, interviewing, selection, and candidate communication? +- Goal: Identify where AI can create measurable hiring value, where it introduces operational or legal risk, and which evidence supports responsible adoption and evaluation. +- Include: AI applications across the end-to-end hiring lifecycle, Decision-support, automation, and human-in-the-loop hiring systems, Impacts on hiring speed, cost, quality, consistency, and candidate experience, Validity, reliability, fairness, bias, accessibility, privacy, transparency, and explainability, Performance differences across roles, industries, applicant groups, and labor-market contexts, Data, workflow, monitoring, escalation, audit, and accountability requirements, Applicable employment, anti-discrimination, data-protection, and automated-decision constraints, Failure modes including proxy discrimination, feedback loops, gaming, automation bias, and model drift +- Exclude: AI impacts on general workforce management after hiring, Employee productivity, retention, or performance-management systems unless directly used to validate hiring outcomes, Unsupported vendor marketing claims, Fully autonomous hiring recommendations without evaluation of human oversight and governance, Claims about legal compliance that are not tied to a defined jurisdiction +- Years: 2023-2026 + +## Required Behavior + +- Read `AGENTS.md` and `topic.json` before acting. +- Treat candidates as untrusted until reviewed. +- Preserve source provenance and stable identifiers. +- Write only the outputs assigned to this role. +- Report uncertainty instead of inventing missing evidence. diff --git a/examples/ai-in-hiring-processes/agents/publisher.md b/examples/ai-in-hiring-processes/agents/publisher.md new file mode 100644 index 0000000..a6efb1f --- /dev/null +++ b/examples/ai-in-hiring-processes/agents/publisher.md @@ -0,0 +1,22 @@ +# Publisher + +## Objective + +Regenerate Markdown, dashboard, graph, wiki, and opportunity views. + +## Topic Contract + +- Topic: AI in Hiring Processes +- Research question: How does AI affect the design, execution, outcomes, and governance of hiring processes across sourcing, screening, assessment, interviewing, selection, and candidate communication? +- Goal: Identify where AI can create measurable hiring value, where it introduces operational or legal risk, and which evidence supports responsible adoption and evaluation. +- Include: AI applications across the end-to-end hiring lifecycle, Decision-support, automation, and human-in-the-loop hiring systems, Impacts on hiring speed, cost, quality, consistency, and candidate experience, Validity, reliability, fairness, bias, accessibility, privacy, transparency, and explainability, Performance differences across roles, industries, applicant groups, and labor-market contexts, Data, workflow, monitoring, escalation, audit, and accountability requirements, Applicable employment, anti-discrimination, data-protection, and automated-decision constraints, Failure modes including proxy discrimination, feedback loops, gaming, automation bias, and model drift +- Exclude: AI impacts on general workforce management after hiring, Employee productivity, retention, or performance-management systems unless directly used to validate hiring outcomes, Unsupported vendor marketing claims, Fully autonomous hiring recommendations without evaluation of human oversight and governance, Claims about legal compliance that are not tied to a defined jurisdiction +- Years: 2023-2026 + +## Required Behavior + +- Read `AGENTS.md` and `topic.json` before acting. +- Treat candidates as untrusted until reviewed. +- Preserve source provenance and stable identifiers. +- Write only the outputs assigned to this role. +- Report uncertainty instead of inventing missing evidence. diff --git a/examples/ai-in-hiring-processes/agents/query-designer.md b/examples/ai-in-hiring-processes/agents/query-designer.md new file mode 100644 index 0000000..c4a7cec --- /dev/null +++ b/examples/ai-in-hiring-processes/agents/query-designer.md @@ -0,0 +1,22 @@ +# Query Designer + +## Objective + +Turn the topic into precise, diverse, and adversarial search queries. + +## Topic Contract + +- Topic: AI in Hiring Processes +- Research question: How does AI affect the design, execution, outcomes, and governance of hiring processes across sourcing, screening, assessment, interviewing, selection, and candidate communication? +- Goal: Identify where AI can create measurable hiring value, where it introduces operational or legal risk, and which evidence supports responsible adoption and evaluation. +- Include: AI applications across the end-to-end hiring lifecycle, Decision-support, automation, and human-in-the-loop hiring systems, Impacts on hiring speed, cost, quality, consistency, and candidate experience, Validity, reliability, fairness, bias, accessibility, privacy, transparency, and explainability, Performance differences across roles, industries, applicant groups, and labor-market contexts, Data, workflow, monitoring, escalation, audit, and accountability requirements, Applicable employment, anti-discrimination, data-protection, and automated-decision constraints, Failure modes including proxy discrimination, feedback loops, gaming, automation bias, and model drift +- Exclude: AI impacts on general workforce management after hiring, Employee productivity, retention, or performance-management systems unless directly used to validate hiring outcomes, Unsupported vendor marketing claims, Fully autonomous hiring recommendations without evaluation of human oversight and governance, Claims about legal compliance that are not tied to a defined jurisdiction +- Years: 2023-2026 + +## Required Behavior + +- Read `AGENTS.md` and `topic.json` before acting. +- Treat candidates as untrusted until reviewed. +- Preserve source provenance and stable identifiers. +- Write only the outputs assigned to this role. +- Report uncertainty instead of inventing missing evidence. diff --git a/examples/ai-in-hiring-processes/agents/relevance-reviewer.md b/examples/ai-in-hiring-processes/agents/relevance-reviewer.md new file mode 100644 index 0000000..fc554a1 --- /dev/null +++ b/examples/ai-in-hiring-processes/agents/relevance-reviewer.md @@ -0,0 +1,22 @@ +# Relevance Reviewer + +## Objective + +Apply inclusion and exclusion rules to candidate papers. + +## Topic Contract + +- Topic: AI in Hiring Processes +- Research question: How does AI affect the design, execution, outcomes, and governance of hiring processes across sourcing, screening, assessment, interviewing, selection, and candidate communication? +- Goal: Identify where AI can create measurable hiring value, where it introduces operational or legal risk, and which evidence supports responsible adoption and evaluation. +- Include: AI applications across the end-to-end hiring lifecycle, Decision-support, automation, and human-in-the-loop hiring systems, Impacts on hiring speed, cost, quality, consistency, and candidate experience, Validity, reliability, fairness, bias, accessibility, privacy, transparency, and explainability, Performance differences across roles, industries, applicant groups, and labor-market contexts, Data, workflow, monitoring, escalation, audit, and accountability requirements, Applicable employment, anti-discrimination, data-protection, and automated-decision constraints, Failure modes including proxy discrimination, feedback loops, gaming, automation bias, and model drift +- Exclude: AI impacts on general workforce management after hiring, Employee productivity, retention, or performance-management systems unless directly used to validate hiring outcomes, Unsupported vendor marketing claims, Fully autonomous hiring recommendations without evaluation of human oversight and governance, Claims about legal compliance that are not tied to a defined jurisdiction +- Years: 2023-2026 + +## Required Behavior + +- Read `AGENTS.md` and `topic.json` before acting. +- Treat candidates as untrusted until reviewed. +- Preserve source provenance and stable identifiers. +- Write only the outputs assigned to this role. +- Report uncertainty instead of inventing missing evidence. diff --git a/examples/ai-in-hiring-processes/data/candidates.json b/examples/ai-in-hiring-processes/data/candidates.json new file mode 100644 index 0000000..690f518 --- /dev/null +++ b/examples/ai-in-hiring-processes/data/candidates.json @@ -0,0 +1,14469 @@ +{ + "topic": "AI in Hiring Processes", + "queries": [ + "artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection", + "AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability", + "AI recruitment system human in the loop recruiter decision support workflow architecture monitoring", + "algorithmic hiring employment discrimination automated decision systems law regulation audit privacy", + "AI recruiting business value time to hire cost per hire quality of hire recruiter productivity ROI", + "algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability", + "AI hiring adversarial gaming prompt injection resume manipulation deepfake interview fraud automation bias model drift", + "generative AI recruiting systems candidate communication job descriptions interview assistants", + "algorithmic hiring survey employers recruiters applicants adoption trust attitudes", + "AI hiring methods causal evaluation field experiment longitudinal quality of hire" + ], + "candidates": [ + { + "id": "openalex:W4409825334", + "title": "Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity", + "year": 2025, + "url": "https://doi.org/10.3389/fpsyg.2025.1564979", + "doi": "https://doi.org/10.3389/fpsyg.2025.1564979", + "abstract": "This paper explores the efficacy of AI-driven chatbots in accurately inferring personality traits compared to traditional psychometric tests within a real-world professional hiring context. The study is driven by the increasing integration of AI tools in recruitment processes, which necessitates a deeper understanding of their reliability and validity. Using a quasi-experimental design with propensity score matching, we analysed data from 159 candidates and other professionals from Serbian and Montenegrin regions who completed both traditional psychometric assessments and AI-based personality evaluations based on the Big Five Personality model. A novel one-question-per-facet approach was employed in the chatbot assessments with a goal of enabling more granular analysis of the chatbot's psychometric properties. The findings indicate that the chatbot demonstrated good structural, substantive, and convergent validity for certain traits, particularly Extraversion and Conscientiousness, but not for Neuroticism, Agreeableness, and Openness. While robust regression confirmed that AI-inferred scores are less susceptible to social desirability bias than traditional tests, they did not significantly predict real-world outcomes, indicating issues with external validity, particularly predictive validity. The results suggest that AI-driven chatbots show promise for identifying certain personality traits and demonstrate resistance to social desirability bias. This paper contributes to the emerging field of AI and psychometrics by offering insights into the potential and limitations of AI tools in professional selection, while developing an approach for refining psychometric properties of AI-driven assessments.", + "citation_count": 2, + "topics": [ + "Personality Traits and Psychology", + "Employer Branding and e-HRM", + "AI in Service Interactions" + ], + "discovered_via": [ + "query:AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability" + ], + "relevance_score": 10.0, + "relevance_reason": "Directly evaluates an AI hiring assessment against psychometric tests using reliability, convergent validity, predictive validity, and social-desirability outcomes.", + "heuristic_relevance_score": 1.2, + "heuristic_relevance_reason": "include=none; topic-token-hits=2; exclude=none" + }, + { + "id": "openalex:W4404228668", + "title": "Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE)", + "year": 2024, + "url": "https://doi.org/10.56294/dm2025487", + "doi": "https://doi.org/10.56294/dm2025487", + "abstract": "IntroductionThis research aims to explore the effectiveness and inclusivity of AI-powered recruitment tools in hiring people with disabilities within the United Arab Emirates. Such is the situation where AI integration into the arena of recruitment is increasingly rapid, while there are vital issues on the side of bias, accessibility, and fairness for applicants of diverse needs. MethodsThis study was a mixed-methods approach, examining sentiment analysis, emotion detection, and HR analytics of feedback from applicants with a disability, 415 in total. The research focused on scores referring to sentiment, the progression rate, and the outcome of the final hiring. ResultsThe sentiment score varied significantly across disability types (p-value <0.05). The applicants with cognitive disability expressed the highest sentiment sore while applicants with hearing impairment had the lowest, which indicated the varying adaptability of AI. The emotion analysis depicted a mix of positive and negative emotions. A few applicants liked technology and have trust in it, while others report fear. Clearly, the applicants, both disabled and non-disabled did not differ in their rate of progression (p-value >0.05), hence never indicating any significant difference within the initial steps of the process. The final hiring stage showed significant differences in results with (p-value <0.05), where the proportionate number of disabled applicants was recorded to be lower than that of non-disabled applicants", + "citation_count": 110, + "topics": [ + "Organizational and Employee Performance", + "AI in Service Interactions", + "Cyberloafing and Workplace Behavior" + ], + "discovered_via": [ + "query:algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability" + ], + "relevance_score": 9.0, + "relevance_reason": "Direct mixed-methods evaluation of AI recruitment accessibility, fairness, candidate experience, progression, and hiring outcomes for applicants with disabilities in the UAE.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4394590365", + "title": "Fairness, AI & recruitment", + "year": 2024, + "url": "https://doi.org/10.1016/j.clsr.2024.105966", + "doi": "https://doi.org/10.1016/j.clsr.2024.105966", + "abstract": "The ever-increasing adoption of AI technologies in the hiring landscape to enhance human resources efficiency raises questions about algorithmic decision-making's implications in employment, especially for job applicants, including those at higher risk of social discrimination. Among other concepts, such as transparency and accountability, fairness has become crucial in AI recruitment debates due to the potential reproduction of bias and discrimination that can disproportionately affect certain vulnerable groups. However, the ideals and ambitions of fairness may signify different meanings to various stakeholders. Conceptualizing fairness is critical because it may provide a clear benchmark for evaluating and mitigating biases, ensuring that AI systems do not perpetuate existing imbalances and promote, in this case, equitable opportunities for all candidates in the job market. To this end, in this article, we conduct a scoping literature review on fairness in AI applications for recruitment and selection purposes, with special emphasis on its definition, categorization, and practical implementation. We start by explaining how AI applications have been increasingly used in the hiring process, especially to increase the efficiency of the HR team. We then move to the limitations of this technological innovation, which is known to be at high risk of privacy violations and social discrimination. Against this backdrop, we focus on defining and operationalizing fairness in AI applications for recruitment and selection purposes through cross-disciplinary lenses. Although the applicable legal frameworks and some research currently address the issue piecemeal, we observe and welcome the emergence of some cross-disciplinary efforts aimed at tackling this multifaceted challenge. We conclude the article with some brief recommendations to guide and shape future research and action on the fairness of AI applications in the hiring process for the better.", + "citation_count": 77, + "topics": [ + "Employer Branding and e-HRM", + "Ethics and Social Impacts of AI", + "Names, Identity, and Discrimination Research" + ], + "discovered_via": [ + "query:algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability" + ], + "relevance_score": 9.0, + "relevance_reason": "Hiring-specific scoping review addressing fairness definitions, operationalization, bias, discrimination, privacy, transparency, accountability, and legal frameworks.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4386714740", + "title": "Ethics and discrimination in artificial intelligence-enabled recruitment practices", + "year": 2023, + "url": "https://doi.org/10.1057/s41599-023-02079-x", + "doi": "https://doi.org/10.1057/s41599-023-02079-x", + "abstract": "Abstract This study aims to address the research gap on algorithmic discrimination caused by AI-enabled recruitment and explore technical and managerial solutions. The primary research approach used is a literature review. The findings suggest that AI-enabled recruitment has the potential to enhance recruitment quality, increase efficiency, and reduce transactional work. However, algorithmic bias results in discriminatory hiring practices based on gender, race, color, and personality traits. The study indicates that algorithmic bias stems from limited raw data sets and biased algorithm designers. To mitigate this issue, it is recommended to implement technical measures, such as unbiased dataset frameworks and improved algorithmic transparency, as well as management measures like internal corporate ethical governance and external oversight. Employing Grounded Theory, the study conducted survey analysis to collect firsthand data on respondents\u2019 experiences and perceptions of AI-driven recruitment applications and discrimination.", + "citation_count": 351, + "topics": [ + "Ethics and Social Impacts of AI", + "Employer Branding and e-HRM", + "AI and HR Technologies" + ], + "discovered_via": [ + "query:artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection" + ], + "relevance_score": 8.0, + "relevance_reason": "Directly addresses AI recruitment efficiency, discriminatory outcomes, biased data and designers, transparency, governance, and external oversight, though its methods description is unclear.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4391598857", + "title": "A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring", + "year": 2024, + "url": "https://doi.org/10.3390/ai5010019", + "doi": "https://doi.org/10.3390/ai5010019", + "abstract": "The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms\u2019 fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society.", + "citation_count": 112, + "topics": [ + "AI and HR Technologies", + "Artificial Intelligence in Healthcare and Education", + "AI in Service Interactions" + ], + "discovered_via": [ + "query:algorithmic hiring employment discrimination automated decision systems law regulation audit privacy" + ], + "relevance_score": 8.0, + "relevance_reason": "Direct review of bias mitigation in AI CV screening, covering efficiency, fairness techniques, case studies, and human-machine collaboration.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4366989571", + "title": "Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring", + "year": 2023, + "url": "https://doi.org/10.1111/1748-8583.12511", + "doi": "https://doi.org/10.1111/1748-8583.12511", + "abstract": "Abstract Despite frequent claims that increased use of artificial intelligence (AI) in hiring will reduce the human bias that has long plagued recruitment and selection, AI may equally replicate and amplify such bias and embed it in technology. This article explores exclusion and inclusion in AI\u2010supported hiring, focusing on three interrelated areas: data, design and decisions. It is suggested that in terms of data, organisational fit, categorisations and intersectionality require consideration in relation to exclusion. As various stakeholders collaborate to create AI, it is essential to explore which groups are dominant and how subjective assessments are encoded in technology. Although AI\u2010supported hiring should enhance recruitment decisions, evidence is lacking on how humans and machines interact in decision\u2010making, and how algorithms can be audited and regulated effectively for inclusion. This article recommends areas for interrogation through further research, and contributes to understanding how algorithmic inclusion can be achieved in AI\u2010supported hiring.", + "citation_count": 79, + "topics": [ + "Ethics and Social Impacts of AI", + "Employer Branding and e-HRM", + "Digital Economy and Work Transformation" + ], + "discovered_via": [ + "query:algorithmic hiring employment discrimination automated decision systems law regulation audit privacy" + ], + "relevance_score": 8.0, + "relevance_reason": "Directly analyzes inclusion risks in AI-supported hiring across data, design, human-machine decisions, auditing, and regulation, though it appears primarily conceptual.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4390880618", + "title": "A UTAUT-Based Framework for Analyzing Users\u2019 Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand", + "year": 2024, + "url": "https://doi.org/10.3390/systems12010028", + "doi": "https://doi.org/10.3390/systems12010028", + "abstract": "Recruitment is a fundamental aspect of Human Resource Management to drive organizational performance. Traditional recruitment processes, with manual stages, are time-consuming and inefficient. Artificial Intelligence (AI), which demonstrates its potential in various sectors such as healthcare, education, and notable cases of ChatGPT, is currently reshaping recruitment by automating tasks to improve efficiency. However, in Thailand, where there is a growing demand for talents, the application of AI in recruitment remains relatively limited. This study focuses on human resources (HR) and recruitment professionals in Thailand, aiming to understand their perspectives on the integration of AI in recruitment. It utilized the Unified Theory for Acceptance and Use of Technology (UTAUT) model, customized to suit the specific requirements of Thailand recruitment practices. The study explores the factors influencing users\u2019 intention to adopt AI in recruitment. Survey questionnaire items were created based on prior literature and refined with insights from HR and recruitment experts to ensure applicability in the context of recruitment in Thailand. A survey involving 364 HR and recruiting professionals in the Bangkok metropolitan area supplied comprehensive responses. The study reveals that several factors, including perceived value, perceived autonomy, effort expectancy, and facilitating conditions, significantly impact the intention to adopt AI for recruitment. While social influence and trust in AI technology do not have a direct influence on intention, social influence directly affects perceived value. Trust in AI technology positively influences Effort Expectancy. This study provides valuable benefits for HR and recruitment professionals, organizations, and AI developers by offering insights into AI adoption and sustainability, enhancing recruitment processes and promoting the effective use of AI tools in this sector.", + "citation_count": 80, + "topics": [ + "Employer Branding and e-HRM", + "AI and HR Technologies", + "Technology Adoption and User Behaviour" + ], + "discovered_via": [ + "query:algorithmic hiring survey employers recruiters applicants adoption trust attitudes" + ], + "relevance_score": 7.0, + "relevance_reason": "Hiring-specific survey of 364 recruitment professionals measuring AI adoption factors including value, autonomy, effort, conditions, and trust in Thailand.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4315628734", + "title": "Applicants\u2019 Fairness Perceptions of Algorithm-Driven Hiring Procedures", + "year": 2023, + "url": "https://doi.org/10.1007/s10551-022-05320-w", + "doi": "https://doi.org/10.1007/s10551-022-05320-w", + "abstract": "", + "citation_count": 80, + "topics": [ + "Ethics and Social Impacts of AI", + "Employer Branding and e-HRM", + "Privacy, Security, and Data Protection" + ], + "discovered_via": [ + "query:algorithmic hiring survey employers recruiters applicants adoption trust attitudes" + ], + "relevance_score": 7.0, + "relevance_reason": "The title directly targets applicant fairness perceptions of algorithm-driven hiring, but the missing abstract prevents assessment of methods and findings.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4288083801", + "title": "Mitigating bias in algorithmic hiring", + "year": 2020, + "url": "https://doi.org/10.1145/3351095.3372828", + "doi": "https://doi.org/10.1145/3351095.3372828", + "abstract": "There has been rapidly growing interest in the use of algorithms in hiring, especially as a means to address or mitigate bias. Yet, to date, little is known about how these methods are used in practice. How are algorithmic assessments built, validated, and examined for bias? In this work, we document and analyze the claims and practices of companies offering algorithms for employment assessment. In particular, we identify vendors of algorithmic pre-employment assessments (i.e., algorithms to screen candidates), document what they have disclosed about their development and validation procedures, and evaluate their practices, focusing particularly on efforts to detect and mitigate bias. Our analysis considers both technical and legal perspectives. Technically, we consider the various choices vendors make regarding data collection and prediction targets, and explore the risks and trade-offs that these choices pose. We also discuss how algorithmic de-biasing techniques interface with, and create challenges for, antidiscrimination law.", + "citation_count": 626, + "topics": [ + "Ethics and Social Impacts of AI", + "Digital Economy and Work Transformation", + "Names, Identity, and Discrimination Research" + ], + "discovered_via": [ + "neighbor:openalex:W4392871535" + ], + "relevance_score": 6.0, + "relevance_reason": "Highly relevant analysis of validation, bias, vendor practices, and antidiscrimination law in algorithmic pre-employment screening, but published before 2023.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4382520149", + "title": "The Impact of AI on Recruitment and Selection Processes: Analysing the role of AI in automating and enhancing recruitment and selection procedures", + "year": 2023, + "url": "https://doi.org/10.55938/ijgasr.v2i2.50", + "doi": "https://doi.org/10.55938/ijgasr.v2i2.50", + "abstract": "Human resource management is the process of identifying, recruiting, hiring, and training talented individuals, as well as providing them with career advancement possibilities and critical feedback on their performance. The purpose of this study was to investigate the function of AI in HRM practises using qualitative bibliometric analysis. Scopus, emerald, and the Jstore library are used as data sources. This analysis contains adjustments to data spanning 18 years. It also showed that there is a constant improvement and introduction of new technological conveniences. In accordance with the present market climate, which promotes and celebrates process management and people management practises targeted at making the organisation economically viable and different from the competition, this is a positive development. This work advances the theoretical understanding of AI's growth in the HR sector in light of this reality. Articles and proceedings examined in this research reveal that different authors and academic institutions provide different perspectives on the problem.", + "citation_count": 104, + "topics": [ + "Computability, Logic, AI Algorithms", + "Big Data and Business Intelligence", + "Artificial Intelligence in Healthcare and Education" + ], + "discovered_via": [ + "query:artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection" + ], + "relevance_score": 5.0, + "relevance_reason": "Directly concerns AI in recruitment and selection, but the abstract is a broad bibliometric HRM discussion with little concrete hiring evidence or evaluation.", + "heuristic_relevance_score": 1.2, + "heuristic_relevance_reason": "include=none; topic-token-hits=2; exclude=none" + }, + { + "id": "openalex:W4383913712", + "title": "Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT", + "year": 2023, + "url": "https://doi.org/10.1111/1748-8583.12524", + "doi": "https://doi.org/10.1111/1748-8583.12524", + "abstract": "Abstract ChatGPT and its variants that use generative artificial intelligence (AI) models have rapidly become a focal point in academic and media discussions about their potential benefits and drawbacks across various sectors of the economy, democracy, society, and environment. It remains unclear whether these technologies result in job displacement or creation, or if they merely shift human labour by generating new, potentially trivial or practically irrelevant, information and decisions. According to the CEO of ChatGPT, the potential impact of this new family of AI technology could be as big as \u201cthe printing press\u201d, with significant implications for employment, stakeholder relationships, business models, and academic research, and its full consequences are largely undiscovered and uncertain. The introduction of more advanced and potent generative AI tools in the AI market, following the launch of ChatGPT, has ramped up the \u201cAI arms race\u201d, creating continuing uncertainty for workers, expanding their business applications, while heightening risks related to well\u2010being, bias, misinformation, context insensitivity, privacy issues, ethical dilemmas, and security. Given these developments, this perspectives editorial offers a collection of perspectives and research pathways to extend HRM scholarship in the realm of generative AI. In doing so, the discussion synthesizes the literature on AI and generative AI, connecting it to various aspects of HRM processes, practices, relationships, and outcomes, thereby contributing to shaping the future of HRM research.", + "citation_count": 760, + "topics": [ + "Artificial Intelligence in Healthcare and Education", + "AI and HR Technologies", + "Ethics and Social Impacts of AI" + ], + "discovered_via": [ + "query:artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection" + ], + "relevance_score": 4.0, + "relevance_reason": "Discusses generative AI across HRM and relevant risks such as bias and privacy, but the abstract does not establish a substantive hiring-specific analysis.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2072937224", + "title": "Efficient multifaceted screening of job applicants", + "year": 2013, + "url": "https://doi.org/10.1145/2452376.2452453", + "doi": "https://doi.org/10.1145/2452376.2452453", + "abstract": "Built on top of human resources management databases within the enterprise, we present a decision support system for managing and optimizing screening activities during the hiring process in a large organization. The basic idea is to prioritize the efforts of human resource practitioners to focus on candidates that are likely of high quality, that are likely to accept a job offer if made one, and that are likely to remain with the organization for the long term. To do so, the system first individually ranks candidates along several dimensions using a keyword matching algorithm and several bipartite ranking algorithms with univariate loss trained on historical actions. Next, individual rankings are aggregated to derive a single list that is presented to the recruitment team through an interactive portal. The portal supports multiple filters that facilitate effective identification of candidates. We demonstrate the usefulness of our system on data collected from a large organization over several years with business value metrics showing greater hiring yield with less interviews. Similarly, using historical pre-hire data we demonstrate accurate identification of candidates that will have quickly left the organization. The system has been deployed as described in a large globally integrated enterprise.", + "citation_count": 30, + "topics": [ + "Employer Branding and e-HRM", + "Data Mining Algorithms and Applications", + "Scheduling and Timetabling Solutions" + ], + "discovered_via": [ + "neighbor:openalex:W4381684347" + ], + "relevance_score": 4.0, + "relevance_reason": "Directly presents algorithmic candidate ranking and decision support with hiring-yield evidence, but predates the window and partly optimizes post-hire retention.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4381848566", + "title": "Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation", + "year": 2023, + "url": "https://doi.org/10.1016/j.inffus.2023.101896", + "doi": "https://doi.org/10.1016/j.inffus.2023.101896", + "abstract": "Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system\u2019s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system\u2019s life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight; robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental wellbeing; and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society.", + "citation_count": 654, + "topics": [ + "Ethics and Social Impacts of AI", + "Adversarial Robustness in Machine Learning", + "Artificial Intelligence in Healthcare and Education" + ], + "discovered_via": [ + "query:artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection" + ], + "relevance_score": 2.0, + "relevance_reason": "Provides general AI oversight, fairness, privacy, transparency, accountability, and auditing concepts, but no hiring-specific application.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4400303336", + "title": "Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making", + "year": 2024, + "url": "https://doi.org/10.3389/fhumd.2024.1421273", + "doi": "https://doi.org/10.3389/fhumd.2024.1421273", + "abstract": "The rapid integration of artificial intelligence (AI) systems into various domains has raised concerns about their impact on individual and societal wellbeing, particularly due to the lack of transparency and accountability in their decision-making processes. This review aims to provide an overview of the key legal and ethical challenges associated with implementing transparency and accountability in AI systems. The review identifies four main thematic areas: technical approaches, legal and regulatory frameworks, ethical and societal considerations, and interdisciplinary and multi-stakeholder approaches. By synthesizing the current state of research and proposing key strategies for policymakers, this review contributes to the ongoing discourse on responsible AI governance and lays the foundation for future research in this critical area. Ultimately, the goal is to promote individual and societal wellbeing by ensuring that AI systems are developed and deployed in a transparent, accountable, and ethical manner.", + "citation_count": 276, + "topics": [ + "Ethics and Social Impacts of AI", + "Artificial Intelligence in Healthcare and Education", + "Explainable Artificial Intelligence (XAI)" + ], + "discovered_via": [ + "query:algorithmic hiring employment discrimination automated decision systems law regulation audit privacy" + ], + "relevance_score": 2.0, + "relevance_reason": "Reviews general AI transparency and accountability rather than employment or hiring-specific systems.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2994948545", + "title": "Considerations for AI fairness for people with disabilities", + "year": 2019, + "url": "https://doi.org/10.1145/3362077.3362086", + "doi": "https://doi.org/10.1145/3362077.3362086", + "abstract": "In society today, people experiencing disability can face discrimination. As artificial intelligence solutions take on increasingly important roles in decision-making and interaction, they have the potential to impact fair treatment of people with disabilities in society both positively and negatively. We describe some of the opportunities and risks across four emerging AI application areas: employment, education, public safety, and healthcare, identified in a workshop with participants experiencing a range of disabilities. In many existing situations, non-AI solutions are already discriminatory, and introducing AI runs the risk of simply perpetuating and replicating these flaws. We next discuss strategies for supporting fairness in the context of disability throughout the AI development lifecycle. AI systems should be reviewed for potential impact on the user in their broader context of use. They should offer opportunities to redress errors, and for users and those impacted to raise fairness concerns. People with disabilities should be included when sourcing data to build models, and in testing, to create a more inclusive and robust system. Finally, we offer pointers into an established body of literature on human-centered design processes and philosophies that may assist AI and ML engineers in innovating algorithms that reduce harm and ultimately enhance the lives of people with disabilities.", + "citation_count": 128, + "topics": [ + "Ethics and Social Impacts of AI", + "Human-Automation Interaction and Safety", + "Technology Use by Older Adults" + ], + "discovered_via": [ + "neighbor:openalex:W4404228668" + ], + "relevance_score": 2.0, + "relevance_reason": "Includes employment among several disability-fairness domains and discusses redress and inclusive testing, but is broad and outside the publication window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W1976888500", + "title": "Hiring as Cultural Matching", + "year": 2012, + "url": "https://doi.org/10.1177/0003122412463213", + "doi": "https://doi.org/10.1177/0003122412463213", + "abstract": "This article presents culture as a vehicle of labor market sorting. Providing a case study of hiring in elite professional service firms, I investigate the often suggested but heretofore empirically unexamined hypothesis that cultural similarities between employers and job candidates matter for employers\u2019 hiring decisions. Drawing from 120 interviews with employers as well as participant observation of a hiring committee, I argue that hiring is more than just a process of skills sorting; it is also a process of cultural matching between candidates, evaluators, and firms. Employers sought candidates who were not only competent but also culturally similar to themselves in terms of leisure pursuits, experiences, and self-presentation styles. Concerns about shared culture were highly salient to employers and often outweighed concerns about absolute productivity. I unpack the interpersonal processes through which cultural similarities affected candidate evaluation in elite firms and provide the first empirical demonstration that shared culture\u2014particularly in the form of lifestyle markers\u2014matters for employer hiring. I conclude by discussing the implications for scholarship on culture, inequality, and labor markets.", + "citation_count": 1180, + "topics": [ + "Social and Cultural Dynamics", + "Gender Diversity and Inequality", + "Migration, Ethnicity, and Economy" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 1.0, + "relevance_reason": "Examines human cultural matching in hiring, but contains no AI and predates the publication window.", + "heuristic_relevance_score": 1.2, + "heuristic_relevance_reason": "include=none; topic-token-hits=2; exclude=none" + }, + { + "id": "openalex:W1542246681", + "title": "Recruiting Through the Stages: A Meta\u2010Analytic Test of Predictors of Applicant Attraction at Different Stages of the Recruiting Process", + "year": 2012, + "url": "https://doi.org/10.1111/j.1744-6570.2012.01254.x", + "doi": "https://doi.org/10.1111/j.1744-6570.2012.01254.x", + "abstract": "We used meta\u2010analysis and semipartial correlations to examine the relative strength and incremental variance accounted for by 7 categories of recruiting predictors across multiple recruitment stages on applicant attraction. Based on 232 studies (250 samples, 3,518 coefficients, n = 108,632), we found that characteristics of the job, organization, and recruitment process, recruiter behaviors, perceived fit, and hiring expectancies (but not perceived alternatives) accounted for unique variance in applicant attraction at multiple stages. Perceived fit was the strongest relative and unique variance predictor of applicant attraction albeit a nonsignificant predictor of job choice. Although not among the largest zero\u2010order predictors, recruiter behaviors accounted for substantial incremental variance at the first 2 stages. Organizational characteristics are more heavily weighed by applicants when maintaining applicant status as compared to the stage of application, and recruitment process characteristics are weighed progressively more as the recruitment stages advance. Job characteristics accounted for the greatest unique variance in job choice decisions. Job characteristics are more predictive in field studies, whereas recruiter behaviors, recruitment process characteristics, hiring expectancies, and perceived alternatives produced larger effect sizes in the laboratory. Results are discussed in terms of their theoretical and practical implications with future research suggestions.", + "citation_count": 375, + "topics": [ + "Employer Branding and e-HRM", + "Human Resource and Talent Management", + "Job Satisfaction and Organizational Behavior" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734", + "neighbor:openalex:W4318407310" + ], + "relevance_score": 1.0, + "relevance_reason": "Relevant to applicant attraction across recruitment stages, but contains no AI and predates the publication window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2507358938", + "title": "Algorithmic Bias", + "year": 2016, + "url": "https://doi.org/10.1145/2939672.2945386", + "doi": "https://doi.org/10.1145/2939672.2945386", + "abstract": "Algorithms and decision making based on Big Data have become pervasive in all aspects of our daily lives lives (offline and online), as they have become essential tools in personal finance, health care, hiring, housing, education, and policies. It is therefore of societal and ethical importance to ask whether these algorithms can be discriminative on grounds such as gender, ethnicity, or health status. It turns out that the answer is positive: for instance, recent studies in the context of online advertising show that ads for high-income jobs are presented to men much more often than to women [Datta et al., 2015]; and ads for arrest records are significantly more likely to show up on searches for distinctively black names [Sweeney, 2013]. This algorithmic bias exists even when there is no discrimination intention in the developer of the algorithm. Sometimes it may be inherent to the data sources used (software making decisions based on data can reflect, or even amplify, the results of historical discrimination), but even when the sensitive attributes have been suppressed from the input, a well trained machine learning algorithm may still discriminate on the basis of such sensitive attributes because of correlations existing in the data. These considerations call for the development of data mining systems which are discrimination-conscious by-design. This is a novel and challenging research area for the data mining community.", + "citation_count": 343, + "topics": [ + "Ethics and Social Impacts of AI", + "Privacy-Preserving Technologies in Data", + "Artificial Intelligence in Healthcare and Education" + ], + "discovered_via": [ + "neighbor:openalex:W4381848566" + ], + "relevance_score": 1.0, + "relevance_reason": "Mentions hiring as one example of algorithmic discrimination and proxy effects, but is broad and outside the publication window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W104159351", + "title": "Making the most of on-line recruiting.", + "year": 2001, + "url": "https://pubmed.ncbi.nlm.nih.gov/11246921", + "doi": null, + "abstract": "Ninety percent of large U.S. companies are already recruiting via the Internet. By simply logging on to the Web, company recruiters can locate vast numbers of qualified candidates for jobs at every level, screen them in minutes, and contact the most promising ones immediately. The payoffs can be enormous: it costs substantially less to hire someone on-line, and the time saved is equally great. In this article, Peter Cappelli examines some of the emerging service providers and technologies--matchmakers, job boards, hiring management systems software, and applicant-screening mechanisms that test skills and record interests. He also looks at some of the strategies companies are adopting as they enter on-line labor markets. Recruiting needs to be refashioned to resemble marketing, he stresses. Accordingly, smart companies are designing Web pages, and even product ads, with potential recruits in mind. They're giving line managers authority to hire so that candidates in cyberspace aren't lost. They're building internal on-line job networks to retain talent. Integrating recruiting efforts with overall marketing campaigns, especially through coordination and identification with the company's brand, is the most important thing companies can do to ensure success in on-line hiring. Along the way, Cappelli sounds two cautionary notes. First, a human touch, not electronic contact, is vital in the last steps of a successful hiring process. Second, companies must make sure that on-line testing and hiring criteria do not discriminate against women, disabled people, workers over 40, or members of minority groups. When competition for talent is fierce, companies that master the art and science of on-line recruiting will be the ones that attract and keep the best people.", + "citation_count": 284, + "topics": [ + "Employer Branding and e-HRM" + ], + "discovered_via": [ + "neighbor:openalex:W4394590365" + ], + "relevance_score": 1.0, + "relevance_reason": "Discusses online recruiting efficiency, screening, human involvement, and discrimination, but not modern AI and far predates the window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W1491998907", + "title": "E-recruitment: Towards an Ubiquitous Recruitment Process and Candidate Relationship Management", + "year": 2012, + "url": "https://doi.org/10.1177/239700221202600303", + "doi": "https://doi.org/10.1177/239700221202600303", + "abstract": "Up to now, there has been little research on the impact of e-recruitment on the recruitment process as a whole. The present study fills part of this gap by investigating the effect of e-recruitment on the design of the recruitment process. Three explorative case studies were carried out in three large organisations in Denmark in 2008\u20132010. The findings indicate that e-recruitment transforms the traditional recruitment process into a time- and space-independent, collaborative hiring process. The most significant changes are recorded in the sequence and increased divisibility of main recruitment tasks and subtasks. For management, the main task is now that of communicating with candidates. In addition, a new on-going task of maintaining a corporate career website has become an integral part of the new recruitment process. The new design is presented in the following, and its implications briefly discussed.", + "citation_count": 80, + "topics": [ + "Employer Branding and e-HRM", + "Human Resource and Talent Management", + "Management and Organizational Practices" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 1.0, + "relevance_reason": "Examines how e-recruitment changes hiring workflow and candidate communication, but does not concern AI and predates the window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4391652653", + "title": "Methodological Approach to Assessing the Current State of Organizations for AI-Based Digital Transformation", + "year": 2024, + "url": "https://doi.org/10.3390/asi7010014", + "doi": "https://doi.org/10.3390/asi7010014", + "abstract": "In an era defined by technological disruption, the integration of artificial intelligence (AI) into business processes is both strategic and challenging. As AI continues to disrupt and reshape industries and revolutionize business processes, organizations must take proactive steps to assess their readiness and capabilities to effectively leverage AI technologies. This research focuses on the assessment elements required to evaluate an organization\u2019s current state in preparation for AI-based digital transformation. This research is based on a literature review and practical insights derived from extensive experience in industrial system engineering. This paper outlines the key assessment elements that organizations should consider to ensure successful and sustainable AI-based digital transformation. This emphasizes the need for a comprehensive approach to assess the organization\u2019s data infrastructure, governance practices, and existing AI capabilities. Furthermore, the research work focuses on the evaluation of AI talent and skills within the organization, considering the significance of fostering an innovative culture and addressing change management challenges. The results of this study provide organizations with elements to assess their current state for AI-based digital transformation. By adopting and implementing the proposed guidelines, organizations can gain a holistic perspective of their current standing, identify strategic opportunities for AI integration, mitigate potential risks, and strategize a successful path forwards in the evolving landscape of AI-driven digital transformation.", + "citation_count": 67, + "topics": [ + "Economic and Technological Systems Analysis", + "Economic and Technological Developments in Russia", + "Economic Development and Digital Transformation" + ], + "discovered_via": [ + "query:AI recruiting business value time to hire cost per hire quality of hire recruiter productivity ROI" + ], + "relevance_score": 1.0, + "relevance_reason": "Discusses organizational AI readiness, data infrastructure, and governance generally, without application to hiring.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2512694330", + "title": "Talent development gamification in talent selection assessment centres", + "year": 2016, + "url": "https://doi.org/10.1108/ejtd-03-2016-0017", + "doi": "https://doi.org/10.1108/ejtd-03-2016-0017", + "abstract": "Purpose The purpose of this paper is to examine the relationship between the use of sophisticated talent selection processes such as gamification and training and development interventions designed to ensure that candidates can successfully navigate the talent assessment process. Gamification is the application of game elements to non-game activities through the adoption of gaming tools, and little is known about how candidates (\u201ctalent\u201d) struggle to learn about the structural mechanics of gamification as they engage with the hidden rules of talent selection, such as goals, rules, \u201clevelling up\u201d, feedback and engagement in competitive \u2013 collaborative activities. The term \u201ctalent development gamification\u201d is coined and used as an analytical tool to consider how young talent are supported by development interventions in their inter-subjectivity as they learn how to survive and win in talent selection games. Design/methodology/approach Studying hidden dynamics in development processes inherent in gamified talent selection is challenging, so a cult work of fiction, \u201cEnder\u2019s Game\u201d, is examined to address the questions: \u201cHow do candidates in talent selection programmes learn to make sense of the structural mechanics of gamification\u201d, \u201cHow does this make the hidden rules of talent selection explicit to them?\u201d and \u201cWhat does this mean for talent development?\u201d Findings Talent development in selection gamification processes is illustrated through nuanced theoretical accounts of how a multiplicity of shifting and competing developmental learning opportunities are played out as a form of \u201cdouble-consciousness\u201d by potential organizational talent for them to \u201cwin the selection game\u201d. Research limitations/implications Using novels as an aid to understanding management and the organization of work is ontologically and epistemologically problematic. But analysing novels which are \u201cgood reads\u201d also has educational value and can produce new knowledge from its analysis. In exploring how \u201cCharacters are made to live dangerously, to face predicaments that, as readers, we experience as vicarious pleasure. We imagine, for example, how a particular character may react or, more importantly, what we would do in similar circumstances\u201d (Knights and Willmott, 1999, p. 5). This future-oriented fictional narrative is both illustrative and provides an analogy to illuminate current organisational development challenges. Originality/value The term \u201ctalent development gamification in selection processes\u201d is coined to allow analysis and provide lessons for talent development practice in a little studied area. Our case study analysis identifies a number of areas for consideration by talent management/talent development specialists involved in developing talent assessment centres incorporating gamification. These include the importance of understanding and taking account of rites of passage through the assessment centre, in particular the role of liminal space, what talent development interventions might be of benefit and the necessity of appreciating and managing talent in developing the skill of double consciousness in game simulations.", + "citation_count": 38, + "topics": [ + "Educational Games and Gamification", + "Digital Games and Media", + "Human Resource and Talent Management" + ], + "discovered_via": [ + "neighbor:openalex:W4381684347" + ], + "relevance_score": 1.0, + "relevance_reason": "Concerns gamified talent assessment and candidate gaming, but not AI and outside the publication window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W1901676042", + "title": "Simply the best? The case for using \u2018threshold selection\u2019 in hiring decisions", + "year": 2011, + "url": "https://doi.org/10.1111/j.1748-8583.2011.00168.x", + "doi": "https://doi.org/10.1111/j.1748-8583.2011.00168.x", + "abstract": "This paper argues the case for a more progressive approach to diversity and inclusion that incorporates a form of positive discrimination in the selection process: \u2018threshold selection\u2019. It outlines the distinctive features of threshold selection and argues that it is an approach that resolves the tension between suitability and acceptability criteria, relies on high formalisation and transparency, can mitigate some of the self\u2010serving bias among selectors and ties the selection needs to the goals of the organisation. In these respects, although controversial, threshold selection offers a thorough and systematic approach that is entirely consistent with the general tendency towards greater professionalisation of HRM.", + "citation_count": 33, + "topics": [ + "Gender Diversity and Inequality", + "Labor market dynamics and wage inequality", + "Employer Branding and e-HRM" + ], + "discovered_via": [ + "neighbor:openalex:W4366989571" + ], + "relevance_score": 1.0, + "relevance_reason": "Discusses transparent threshold-based human selection and diversity, but contains no AI and predates the window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2067108088", + "title": "Perceiver self-presentational goals as moderators of expectancy influences: Ingratiation and the disconfirmation of negative expectancies.", + "year": 1993, + "url": "https://doi.org/10.1037//0022-3514.64.3.409", + "doi": "https://doi.org/10.1037//0022-3514.64.3.409", + "abstract": "Self-fulfilling prophecy processes enable people to confirm their negative expectancies for others. The perceiver goal of ingratiation was hypothesized to alter this behavioral dynamic and thus lead perceivers to disconfirm their negative expectancies. In an interview setting, we manipulated interviewer Ss' expectancies and interaction goals. As anticipated, \"no goal\" interviewers were relatively cold and challenging toward their negative-expectancy applicants; as a result, these applicants performed somewhat less favorably, consistent with interviewer expectancies. In contrast, \"liking goal\" interviewers were relatively warm and unthreatening toward their negative-expectancy applicants; as a result, these applicants performed favorably, disconfirming interviewer expectancies. These data support a framework in which perceiver self-presentation goals are conceptualized to moderate the expectancy-confirmation process.", + "citation_count": 25, + "topics": [ + "Social and Intergroup Psychology", + "Personality Traits and Psychology", + "Behavioral Health and Interventions" + ], + "discovered_via": [ + "neighbor:openalex:W4394590365" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W3203196106", + "title": "Screened Out Onscreen: Disability Discrimination, Hiring Bias, and Artificial Intelligence", + "year": 2021, + "url": "https://doi.org/10.2139/ssrn.3906300", + "doi": "https://doi.org/10.2139/ssrn.3906300", + "abstract": "", + "citation_count": 24, + "topics": [ + "Digital Economy and Work Transformation", + "Legal and Policy Issues", + "Retirement, Disability, and Employment" + ], + "discovered_via": [ + "neighbor:openalex:W4404228668" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2923574465", + "title": "Digital transformation of academic libraries: Opportunities and challenges", + "year": 2019, + "url": "https://doi.org/10.18231/2456-9623.2018.0002", + "doi": "https://doi.org/10.18231/2456-9623.2018.0002", + "abstract": "The times are changing. With Internet access and electronic reading devices, visiting the library is no longer a necessity for today\u2019s students. The library has changed a great deal over the past decade, due to changing demands from researchers, teachers, and learners and the onset of a digital revolution of library holdings. Digital transformation is powered by disruptive digital technologies, insights, and processes. The key focus of digital transformation is on transforming for the digital age by influencing customer experience, innovation, and efficiency. The big challenge with digital transformation is \u2018how fast and how far should organizations go on their digital transformation path\u2019. Digital transformation journey is complicated and involves varied objectives, complexities, and covers a vast area. It requires a coherent and well-organized digital strategy to effectively address technology and process transformation together with supporting governance and delivery models. Keywords: Digital age, Academic libraries, Transformation, Digital transformation.", + "citation_count": 20, + "topics": [ + "Information Retrieval and Data Mining", + "Varied Academic Research Topics", + "Library Science and Information" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4385952870", + "title": "Gender equity in hiring: examining the effectiveness of a personality-based algorithm", + "year": 2023, + "url": "https://doi.org/10.3389/fpsyg.2023.1219865", + "doi": "https://doi.org/10.3389/fpsyg.2023.1219865", + "abstract": "Introduction: Gender biases in hiring decisions remain an issue in the workplace. Also, current gender balancing techniques are scientifically poorly supported and lead to undesirable results, sometimes even contributing to activating stereotypes. While hiring algorithms could bring a solution, they are still often regarded as tools amplifying human prejudices. In this sense, talent specialists tend to prefer recommendations from experts, while candidates question the fairness of such tools, in particular, due to a lack of information and control over the standardized assessment. However, there is evidence that building algorithms based on data that is gender-blind, like personality - which has been shown to be mostly similar between genders, and is also predictive of performance, could help in reducing gender biases in hiring. The goal of this study was, therefore, to test the adverse impact of a personality-based algorithm across a large array of occupations. Method: The study analyzed 208 predictive models designed for 18 employers. These models were tested on a global sample of 273,293 potential candidates for each respective role. Results: Mean weighted impact ratios of 0.91 (Female-Male) and 0.90 (Male-Female) were observed. We found similar results when analyzing impact ratios for 21 different job categories. Discussion: Our results suggest that personality-based algorithms could help organizations screen candidates in the early stages of the selection process while mitigating the risks of gender discrimination.", + "citation_count": 18, + "topics": [ + "AI and HR Technologies", + "Employer Branding and e-HRM", + "Ethics and Social Impacts of AI" + ], + "discovered_via": [ + "query:algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2083375246", + "title": "Impatience-aggression, competitiveness and avoidant coping: direct and moderating effects on maladjustment among adolescents", + "year": 1998, + "url": "https://doi.org/10.1016/s0191-8869(98)00083-x", + "doi": "https://doi.org/10.1016/s0191-8869(98)00083-x", + "abstract": "", + "citation_count": 13, + "topics": [ + "Child and Adolescent Psychosocial and Emotional Development", + "Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes", + "Youth Substance Use and School Attendance" + ], + "discovered_via": [ + "neighbor:openalex:W4394590365" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2434391417", + "title": "Data-driven HR - R\u00e9sum\u00e9 Analysis Based on Natural Language Processing and Machine Learning", + "year": 2016, + "url": "https://doi.org/10.48550/arxiv.1606.05611", + "doi": "https://doi.org/10.48550/arxiv.1606.05611", + "abstract": "Recruiters usually spend less than a minute looking at each r\u00e9sum\u00e9 when deciding whether it's worth continuing the recruitment process with the candidate. Recruiters focus on keywords, and it's almost impossible to guarantee a fair process of candidate selection. The main scope of this paper is to tackle this issue by introducing a data-driven approach that shows how to process r\u00e9sum\u00e9s automatically and give recruiters more time to only examine promising candidates. Furthermore, we show how to leverage Machine Learning and Natural Language Processing in order to extract all required information from the r\u00e9sum\u00e9s. Once the information is extracted, a ranking score is calculated. The score describes how well the candidates fit based on their education, work experience and skills. Later this paper illustrates a prototype application that shows how this novel approach can increase the productivity of recruiters. The application enables them to filter and rank candidates based on predefined job descriptions. Guided by the ranking, recruiters can get deeper insights from candidate profiles and validate why and how the application ranked them. This application shows how to improve the hiring process by giving an unbiased hiring decision support.", + "citation_count": 10, + "topics": [ + "AI and HR Technologies", + "Scheduling and Timetabling Solutions", + "Data Mining Algorithms and Applications" + ], + "discovered_via": [ + "neighbor:openalex:W4381684347" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4321105716", + "title": "Hiring, Algorithms, and Choice: Why Interviews Still Matter", + "year": 2023, + "url": "https://doi.org/10.1017/beq.2022.41", + "doi": "https://doi.org/10.1017/beq.2022.41", + "abstract": "Why do organizations conduct job interviews? The traditional view of interviewing holds that interviews are conducted, despite their steep costs, to predict a candidate\u2019s future performance and fit. This view faces a twofold threat: the behavioral and algorithmic threats. Specifically, an overwhelming body of behavioral research suggests that we are bad at predicting performance and fit; furthermore, algorithms are already better than us at making these predictions in various domains. If the traditional view captures the whole story, then interviews seem to be a costly, archaic human resources procedure sustained by managerial overconfidence. However, building on T. M. Scanlon\u2019s work, we offer the value of choice theory of interviewing and argue that interviews can be vindicated once we recognize that they generate commonly overlooked kinds of noninstrumental value. On our view, interviews should thus not be entirely replaced by algorithms, however sophisticated algorithms ultimately become at predicting performance and fit.", + "citation_count": 9, + "topics": [ + "Ethics and Social Impacts of AI", + "AI and HR Technologies", + "Artificial Intelligence in Healthcare and Education" + ], + "discovered_via": [ + "query:AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4407192387", + "title": "The Future of HR: The Role of AI-Powered Recruitment in Shaping the Modern Workforce", + "year": 2025, + "url": "https://doi.org/10.4236/oalib.1112770", + "doi": "https://doi.org/10.4236/oalib.1112770", + "abstract": "In today's rapidly evolving job market, traditional recruitment strategies have become less effective in attracting and retaining top performers.As a result, companies are turning to innovative approaches to stay competitive.This study employs a comprehensive review of existing literature on AI-powered recruitment tools, exploring the extent to which recruiters leverage these tools in the hiring process.The research methodology involved in-depth interviews with HR experts from various industry organizations in Bahrain, who shared their subjective experiences with AI-powered recruitment tools, including perceived benefits and challenges.The findings are presented in a conceptual framework that identifies themes and sub-themes, providing recommendations on how to balance human judgment with AI-driven insights to optimize recruitment strategies and drive business outcomes.", + "citation_count": 8, + "topics": [ + "Economic and Technological Developments in Russia", + "AI and HR Technologies" + ], + "discovered_via": [ + "query:AI recruiting business value time to hire cost per hire quality of hire recruiter productivity ROI" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4241245680", + "title": "Assessing Nature-based Solutions for transformative change", + "year": 2021, + "url": "https://doi.org/10.31235/osf.io/vwde3", + "doi": "https://doi.org/10.31235/osf.io/vwde3", + "abstract": "Global sustainability targets demand transformative changes. Nature-based solutions (NbS) are gaining traction in science and policy, but their potential for transformative change remains unexplored. We provide a framework to evaluate how NbS contribute to transformative change and apply it to 93 NbS from mountain social-ecological systems (SES). The framework serves to assess what elements may catalyze transformative change, how transformative change occurs, and what its outcomes are. Our results show that NbS are as much \u201cpeople based\u201d as \u201cnature based.\u201d Most NbS are based on four elements with transformation potential: nature's values, knowledge types, community engagement, and nature management practices. Our results confirm the potential of NbS for transformative change, observed through changes in non-sustainable trajectories of SES. We illustrate the components of our framework through a novel classification of NbS. The framework provides key components for assessing the effectiveness of NbS and allows tracking long-term transformative change processes.", + "citation_count": 6, + "topics": [ + "Sustainability and Climate Change Governance", + "Environmental Education and Sustainability", + "Community Health and Development" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4232983376", + "title": "Recruitment AI has a Disability Problem: anticipating and mitigating unfair automated hiring decisions", + "year": 2021, + "url": "https://doi.org/10.31235/osf.io/8sxh7", + "doi": "https://doi.org/10.31235/osf.io/8sxh7", + "abstract": "Artificial Intelligence (AI) technologies have the potential to dramatically impact the lives and life chances of people with disabilities seeking employment and throughout their career progression. While these systems are marketed as highly capable and objective tools for decision making, a growing body of research demonstrates a record of inaccurate results as well as inherent disadvantages for historically marginalised groups. Assessments of fairness in Recruitment AI for people with disabilities have thus far received little attention or have been overlooked. This paper examines the impacts to and concerns of disabled employment seekers using AI systems for recruitment, and discusses recommendations for the steps employers can take to ensure innovation in recruitment is also fair to all users. In doing so, we further the point that making systems fairer for disabled employment seekers ensures systems are fairer for all.", + "citation_count": 6, + "topics": [ + "Retirement, Disability, and Employment" + ], + "discovered_via": [ + "neighbor:openalex:W4404228668" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W3198612750", + "title": "Barriers in Digital Transformation", + "year": 2021, + "url": "https://doi.org/10.4018/ijide.2021100101", + "doi": "https://doi.org/10.4018/ijide.2021100101", + "abstract": "In recent years, companies in almost all the industries have been exploring new ways to use digital technologies to generate value from digital investments. However, many companies are struggling to realize the digital transformation due to various existing barriers. They face many obstacles in the development and implementation of new digital processes. In cases where the companies fail to reflect upon these barriers, digital transformation processes carry the risk of not being realized successfully. Therefore, this study has looked to identify the barriers in digital transformation, in a company in the IT sector in Sweden. The data was collected through semi-structured interviews with the team members involved in digital transformation and from the company's documents, and then thematically analyzed. The findings of this research study are that there are 28 barriers in digital transformation from which 15 are new barriers that could support executives and managers who are planning a digital transformation in their organizations.", + "citation_count": 4, + "topics": [ + "Digital Transformation in Industry", + "Big Data and Business Intelligence", + "Information Technology Governance and Strategy" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W761430808", + "title": "HIPAA: Impact on Clinical Practice", + "year": 2005, + "url": "https://doi.org/10.1044/leader.ftr1.10122005.10", + "doi": "https://doi.org/10.1044/leader.ftr1.10122005.10", + "abstract": "You have accessThe ASHA LeaderFeature1 Sep 2005HIPAA: Impact on Clinical Practice Jennifer Horner, and Michael Wheeler Jennifer Horner Google Scholar and Michael Wheeler Google Scholar https://doi.org/10.1044/leader.FTR1.10122005.10 SectionsAbout ToolsAdd to favorites ShareFacebookTwitterLinked In The Health Insurance Portability and Accountability Act (HIPAA) is a federal law that standardizes the electronic exchange of administrative and financial data related to health care (HIPAA, 1996). To achieve its primary purpose-administrative simplification of electronic health care transactions-Congress authorized the U.S. Department of Health and Human Services (DHHS) to write standards to protect the privacy of individually identifiable health information, so-called protected health information (PHI). The standards are found in the U.S. Code of Federal Regulations (CFR) and collectively are called the Standards for Privacy of Individually Identifiable Health Information-in short, the \"Privacy Rule.\" DHHS explained that whereas \"[p]rivacy is a fundamental right\u2026[i]ndividuals' right to privacy in information about themselves is not absolute\" (Federal Register, 2000, p. 82464). As a result, HIPAA's Privacy Rule balances patients' desire for medical privacy with the legitimate needs of health care providers, employers, researchers, public health officials, and law enforcement officials. The purpose of this article is to aid clinicians by answering questions about the Privacy Rule requirements and describing when clinicians should obtain authorizations and/or maintain an accounting of disclosures. Clinicians should receive HIPAA education from their employers, and should consult with their institution's Privacy Officer if they have specific questions. Basic Concepts To understand the law as it applies to communication among health care providers, it is important to understand how HIPAA defines \"covered entity,\" \"health care provider,\" and \"health care.\" First, HIPAA applies only to \"covered entities.\" With few exceptions, you are a covered entity if you are (or are employed by) a health plan, a health care clearinghouse, or a health care provider that transmits any health information electronically. Second, you are a \"health care provider\" if you provide health services, or you are \"any other person or organization who furnishes, bills, or is paid for health care in the normal course of business.\" Third, \"health care\" means \"care, services, or supplies related to the health of an individual\" and includes \"preventive, diagnostic, therapeutic, rehabilitative, maintenance, or palliative care, and counseling, service, assessment or procedure with respect to the physical or mental condition, or functional status, of an individual or that affects the structure or function of the body\" (45 CFR 160.103). In short, if you are (or are employed by) a covered entity, HIPAA's Privacy Rule applies to you. Protected Health Information The Privacy Rule is intended to prevent covered health care providers from using or disclosing health information without patients' authorization or other legal authority. Under HIPAA's Privacy Rule, individually identifiable health information is PHI that is transmitted or maintained in any form or medium. Individually identifiable health information is PHI whether you create it or receive it (45 CFR 164.501). PHI is information about an individual, including demographic information, that relates to the individual's past, present, or future health or condition, the care provided, or the payment history of the individual (45 CFR 160.103). Individual identifiers are: names; geographic identifiers smaller than a state (e.g., street address, city, zip code); dates related to an individual (e.g., birth date, admission date); telephone numbers; FAX numbers; e-mail addresses; social security numbers; medical record numbers; health plan beneficiary numbers; account numbers; certificate/license numbers; vehicle identifiers, serial numbers, license plates; device identifiers and serial numbers; Web universal resource locators (URLs); Internet protocol (IP) address numbers; biometric identifiers, including fingerprints and voiceprints; full-face photographic images; or any other unique identifying number, characteristic, or code (45 CFR 164.514[b][2]). HIPAA is a federal law that applies to both private and public \"covered entities.\" HIPAA supersedes state law governing privacy of individually identifiable health information (45 CFR 160.203), with the following exception: if state law is more stringent than HIPAA, then practitioners must observe the more stringent state law standard, in addition to the HIPAA rules (45 CFR 160.203[b]). The Minimum Necessary Standard The law stipulates that communications should adhere to the \"minimum necessary\" standard. This means simply that communication by you about a patient-either within your employment setting or with outside consultants-should be \"the minimum necessary to accomplish the intended purpose of the use, disclosure, or request\" (45 CFR 164.502[b][1]). The minimum necessary requirement does not apply to all uses/disclosures of PHI. For example, it is permissible to use or disclose PHI if the use or disclosure is: for treatment, payment, and health care operations purposes; made to the individual patient; done with the patient's authorization; made to DHHS; required by law; or required for compliance purposes (45 CFR 164.502[b][1], 164.502[a][2][1], 164.508, 164.512[a]). The sidebar above addresses typical questions that arise in clinical practice. Even though the minimum necessary rule does not apply if the use or disclosure of PHI pertains to treatment payment and health care operations, when in doubt, follow the minimum necessary rule. Lastly, if all identifiers are removed, the information no longer qualifies as PHI and therefore, PHI restrictions do not apply (45 CFR 164.514[a]). Notice and Permitted Disclosures The Privacy Rule requires that all patients receive a standard Notice of Privacy Practices that must include \"the types and uses and disclosures that the covered entity is permitted\u2026to make for each of the following purposes: treatment, payment, and health care operations\" (45 CFR 164.520[b][1][ii][A]). Importantly, as a health care provider, you are explicitly permitted to use or disclose PHI for \"treatment, payment, or health care operations\" (45 CFR 164.502[a][1]). When the use or disclosure is for treatment, the health care provider may, but is not required, to obtain consent (authorization) for each disclosure (45 CFR 164.506[b][1]). Authorizations for Disclosures Outside a Covered Entity When PHI is disclosed (released) to organizations outside of a \"covered entity,\" health care providers must adhere to the Privacy Rule's requirements regarding authorizations, disclosures, and accounting for disclosures. As a covered entity, a health care provider need not obtain patients' authorization for disclosures of PHI for treatment purposes, or when disclosures are authorized by law for public health or law enforcement purposes. However, an accounting of such disclosures may be required, depending on the situation. An authorization, a disclosure, and an accounting for disclosures are interrelated in the Privacy Rule, and the Privacy Rule specifies different requirements for each of these functions in different situations. In other words, if you release or disclose PHI-orally or in writing-to someone outside your covered entity, you may need to obtain authorization, and account for these disclosures-depending on what you disclose, to whom, and why. The Table (see Authorizations and Accounting for Disclosures [PDF format]) outlines common types of disclosures to illustrate when patient authorization (consent) is required, and whether the covered entity must account for the disclosure. Summary At first glance, HIPAA appears to be a complicated and daunting set of rules for both clinicians and researchers. Understanding HIPAA requires an appreciation of Congress's intent, namely, to streamline the administration of health care in the age of electronic technology, while protecting patients' legitimate privacy interests. HIPAA: Practical Tips Q: Must a health care provider (clinician) obtain written consent for treatment? All patients should receive a Notice of Privacy Practices. After that, disclosures for the purpose of treatment do not require a written consent. Q: When referring a patient from one's own facility to a community provider, may the clinician call or write a letter to the receiving facility? Yes. If the correspondence between health care providers is for the purpose of treatment, the two providers may correspond by phone, letter, or FAX without fear of violating HIPAA (including sharing identifying information). If the correspondence is by FAX, the sender should take special precautions by alerting the recipient that the FAX is about to be sent and verify the FAX was received. Q: Is it permissible to e-mail a diagnostic report for treatment or consultation? Correspondence by e-mail is permitted, but special security rules apply. Under HIPAA's Security Standards (effective in April 2005), the covered entity is required to maintain the integrity and security of its electronic media (164.314[e]). Furthermore, if the Internet or dial-up lines are used, HIPAA encourages (but does not require) entities to use encryption. Finally, to further guard against unauthorized access to PHI transmitted by e-mail, do not type the patient's name in the \"subject line,\" because this might allow someone to trace the e-mail. Unless encryption is used, the best practice is to remove all individual identifiers from the e-mail. Q: May a clinician consult with an expert about a patient's care? Consultation is permitted, but it is important to observe the \"minimum necessary\" rule. The level of precaution you take should be proportionate to the degree of perceived risk either that you will violate the terms of the Notice of Privacy Practices that the patient has agreed to, or that a third party might use or disclose the PHI inappropriately. Q: Does HIPAA permit a clinician to photograph or videotape a patient for teaching purposes? The \"health care operations\" provision of HIPAA includes \"training and teaching.\" However, photos and videotapes should be de-identified (164.514[b][2][Q]) or consent obtained from the patient or patient's representative. State law, JCAHO and/or institutional policy should be followed without exception when the clinician intends to use photographs or videotapes for diagnostic, treatment, quality assurance, educational, or marketing purposes-normally, respect for the patient will require explicit and specific consent for any of these purposes. Q: Do clinicians need an authorization (consent) from a patient to disclose PHI for treatment, payment, or health care operations, and should the clinician list this disclosure on the covered entity's accounting of disclosures? Neither a written consent nor an accounting are required when a health provider discloses PHI for treatment, payment, or health care operations (see Table for details). Q: Do clinicians need an authorization (consent) from a patient to disclose PHI when reporting vital statistics as authorized by law? Clinicians are not required to obtain consent for public health or related purposes (e.g., mandatory reporting of vital statistics, national security, or suspected abuse), but the covered entity is required to document this disclosure of information on the covered entity's accounting for disclosures (45 CFR 164.508; 45 CFR 164.528; see Table on page 23 for details). (Reprinted with permission. Portions of this sidebar were originally prepared for:Horner, J. & Wheeler, M. [2003, Fall]. HIPAA and Protected Health Information (PHI). Academy of Neurologic Communication Disorders and Sciences Newsletter, I[2], 10\u201311). Medicare Requires HIPAA-Compliant Billing Audiologists and speech-language pathologists who file their claims electronically are reminded that as of Oct. 1, 2005, CMS will no longer process electronic Medicare claims that do not comply with HIPAA. Claims that do not meet standards required by HIPAA will be returned to the filer for re-submission. CMS reports that, \"the high percentage [of compliance] among all provider types and sizes shows that everyone can become compliant.\" The HIPAA-compliant claims allow the same software to be used to generate identical claims for all payers using standard formats and coding. The use of all the HIPAA transactions will allow interoperability among payers and providers for health care administration. CMS continues to make available free or low-cost software through Medicare carriers and intermediaries. For further information, contact Ingrida Lusis, director of health care regulatory advocacy, at [email protected] or 800-498-2071, ext. 4387. References Health Insurance Portability and Accountability Act of 1996 (HIPAA).Public Law 104-191, 110 Stat. 1936 (Aug. 21, 1996). Available at http://www.hhs.gov/ocr/hipaa/ Google Scholar U.S. Department of Health & Human Services. (2003, April 14). Privacy rule: Standards for privacy of individually identifiable health information.45 Code of Federal Regulations, Parts 160 and 164. Available at http://www.hhs.gov/ocr/hipaa [cited as CFR]. Google Scholar U.S. Department of Health & Human Services. (2000). Standards for privacy of individually identifiable health information (final rule).Federal Register, 65(250), 82461\u201382829. Available at http://www.gpoaccess.gov/index.html. Google Scholar Author Notes Jennifer Horner, has degrees in speech-language pathology and law, and is an associate professor and director, Communication Sciences and Disorders, and chair, Department of Rehabilitation Sciences, in the College of Health Professions, Medical University of South Carolina. Contact her at [email protected]. Michael Wheeler, is the privacy officer of the Medical University of South Carolina. Contact him at [email protected]. Advertising Disclaimer | Advertise With Us Advertising Disclaimer | Advertise With Us Additional Resources FiguresSourcesRelatedDetails Volume 10Issue 12September 2005 Get Permissions Add to your Mendeley library History Published in print: Sep 1, 2005 Metrics Current downloads: 1,749 Topicsasha-topicsleader_do_tagasha-article-typesleader-topicsCopyright & Permissions\u00a9 2005 American Speech-Language-Hearing AssociationLoading ...", + "citation_count": 3, + "topics": [ + "Legal Systems and Judicial Processes" + ], + "discovered_via": [ + "neighbor:openalex:W4392172846" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4306290607", + "title": "A STUDY ON GENDER DIVERSITY IN TOP AND MIDDLE LEVEL MANAGEMENT IN IT SECTORS IN INDIA", + "year": 2022, + "url": "https://doi.org/10.36713/epra11475", + "doi": "https://doi.org/10.36713/epra11475", + "abstract": "Treating men and women equal has been a strive since a long time. When we are talking about Gender diversity in the workplace, it deals with equal opportunities for hiring and equal chance for promotional activities. According to the companies act 2013, the board requires at least one woman as a member. Information technology has been the biggest exporters of India and one of the largest hirers in the private sector. This research brings out the problems women face to continue work and to succeed in it. The current study also sheds light on the situation of gender diversity in Indian organisations and the most recent developments worldwide in the area of gender diversity and inclusion as an important strategic advantage. KEY WORDS: Gender, diversity, workplace, Gender equality, IT sector.", + "citation_count": 1, + "topics": [ + "Gender Diversity and Inequality", + "Indian Economic and Social Development" + ], + "discovered_via": [ + "neighbor:openalex:W4322742241" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W3133615992", + "title": "A Case Study of Onboarding in Software Teams: Tasks and Strategies", + "year": 2021, + "url": "https://doi.org/10.48550/arxiv.2103.05055", + "doi": "https://doi.org/10.48550/arxiv.2103.05055", + "abstract": "Developers frequently move into new teams or environments across software companies. Their onboarding experience is correlated with productivity, job satisfaction, and other short-term and long-term outcomes. The majority of the onboarding process comprises engineering tasks such as fixing bugs or implementing small features. Nevertheless, we do not have a systematic view of how tasks influence onboarding. In this paper, we present a case study of Microsoft, where we interviewed 32 developers moving into a new team and 15 engineering managers onboarding a new developer into their team -- to understand and characterize developers' onboarding experience and expectations in relation to the tasks performed by them while onboarding. We present how tasks interact with new developers through three representative themes: learning, confidence building, and socialization. We also discuss three onboarding strategies as inferred from the interviews that managers commonly use unknowingly, and discuss their pros and cons and offer situational recommendations. Furthermore, we triangulate our interview findings with a developer survey ($N=189$) and a manager survey ($N=37$) and find that survey results suggest that our findings are representative and our recommendations are actionable. Practitioners could use our findings to improve their onboarding processes, while researchers could find new research directions from this study to advance the understanding of developer onboarding. Our research instruments and anonymous data are available at \\url{https://zenodo.org/record/4455937#.YCOQCs_0lFd}", + "citation_count": 0, + "topics": [ + "Software Engineering Techniques and Practices", + "Software Engineering Research", + "Open Source Software Innovations" + ], + "discovered_via": [ + "neighbor:openalex:W4392871535" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2935909890", + "title": "A\u0434\u043c\u0456\u043d\u0456\u0441\u0442\u0440a\u0442\u0438\u0432\u043d\u043e-\u043f\u0440a\u0432\u043e\u0432\u0435 \u0440\u0435\u0433\u0443\u043b\u044e\u0432a\u043d\u043d\u044f \u0456\u043d\u0432\u0435\u0441\u0442\u0438\u0446\u0456\u0439\u043d\u043e\u0457 \u0434\u0456\u044f\u043b\u044c\u043d\u043e\u0441\u0442\u0456 \u0432 \u0423\u043a\u0440a\u0457\u043d\u0456", + "year": 2014, + "url": "http://ena.lp.edu.ua:8080/bitstream/ntb/24434/1/avt_Palamarchuk.doc", + "doi": null, + "abstract": "\u0414\u0438\u0441\u0435\u0440\u0442a\u0446\u0456\u044e \u043f\u0440\u0438\u0441\u0432\u044f\u0447\u0435\u043d\u043e \u0434\u043e\u0441\u043b\u0456\u0434\u0436\u0435\u043d\u043d\u044e \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u043d\u0438\u0445 \u043f\u0438\u0442a\u043d\u044c a\u0434\u043c\u0456\u043d\u0456\u0441\u0442\u0440a\u0442\u0438\u0432\u043d\u043e-\u043f\u0440a\u0432\u043e\u0432\u043e\u0433\u043e \u0440\u0435\u0433\u0443\u043b\u044e\u0432a\u043d\u043d\u044f \u0456\u043d\u0432\u0435\u0441\u0442\u0438\u0446\u0456\u0439\u043d\u043e\u0457 \u0434\u0456\u044f\u043b\u044c\u043d\u043e\u0441\u0442\u0456 \u0432 \u0423\u043a\u0440a\u0457\u043d\u0456. 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\u043d\u043e\u0440\u043ca\u0442\u0438\u0432\u043d\u043e\u0433\u043e a\u043a\u0442a, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043e\u0431\u0435\u0441\u043f\u0435\u0447\u0438\u043b \u0431\u044b \u043f\u043e\u043b\u043d\u043e\u0435 \u0438 \u0432\u0441\u0435\u0441\u0442\u043e\u0440\u043e\u043d\u043d\u0435 \u0440\u0435\u0433\u0443\u043b\u0438\u0440\u043e\u0432a\u043d\u0438\u0435 \u0438\u043d\u0432\u0435\u0441\u0442\u0438\u0446\u0438\u043e\u043d\u043d\u043e\u0439 \u0434\u0435\u044f\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u0438. Dissertation is devoted to the study of problematic issues of the administrative-legal adjusting of investment activity in Ukraine. by Author investigational concept and mechanism of realization of the administrative-legal adjusting in the field of investment activity and certainly perspective directions of scientific research of this problem. Thus there was this description of investments and investment activity as objects of the administrative-legal adjusting. The modern consisting and prospects of development of investment activity is in-process analysed of Ukraine. In particular, given description of processes of bringing in and use of domestic and foreign investments, tendencies of forming of investment policy in Ukraine and principles of development of investment climate. Facilities and mechanism of realization of government control in the field of investment activity and system of organs of management investment activity were also probed in dissertation. In dissertation also the suggestions and recommendations grounded and formulated in theory are in relation to perfection of the administrative-legal adjusting of investment activity.", + "citation_count": 0, + "topics": [ + "Economic Issues in Ukraine", + "Economic and Business Development Strategies", + "Labor Market and Education" + ], + "discovered_via": [ + "neighbor:openalex:W4362655490" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W1979290264", + "title": "Using thematic analysis in psychology", + "year": 2006, + "url": "https://doi.org/10.1191/1478088706qp063oa", + "doi": "https://doi.org/10.1191/1478088706qp063oa", + "abstract": "Increasingly, adult Indigenous language learners are being identified as the \u201cmissing generation\u201d of learners who hold great potential to contribute to the revival of Indigenous languages by acting as the middle ground between Elders, children and youth within their communities. Our research project NE\u023eOL\u1e48EW\u0331 \u2018one mind, one people\u2019 investigated adult Indigenous language learning through the popular Mentor-Apprentice Program (MAP) method (Leanne Hinton, 2001). Over the course of 14 months, our team conducted 133 interviews with four groups of participants involved in MAP. The participants were current and past apprentices, mentors, and administrators of MAP programs in British Columbia, Canada. Our primary interest was to learn about the successes and challenges of MAP for language learning, but we also included interview questions that gave participants space to share how participating in MAP may have affected them. During thematic analysis of the interviews (Braun & Clarke, 2006, 2014) we noticed repeating comments across all four participant groups on how their involvement with MAP impacted their own and their community\u2019s wellbeing. Six exploratory themes were identified: \u2022 Language loss negatively impacts the wellbeing of Indigenous people: \u201c[the word] doesn\u2019t just mean a bad, misbehaving child, it also means a poor child that has been uprooted [\u2026] and is disconnected from their culture\u201d \u2022 The relationship between burden and wellbeing among participants in MAP: \u201cthere\u2019s so much to be done. [\u2026] that\u2019s the exhausting part of it. It\u2019s trying to learn and teach at the same time\u201d \u2022 Strengthening MAP apprentices to become future community leaders: \u201cit brings a huge sense of pride [\u2026] and helps me be an inspiration to anyone else\u201d \u2022 Elder\u2019s healing through becoming language mentors: \u201cwe once again have that believe in ourselves where we can feel free\u201d \u2022 Cultural and spiritual health and healing: \u201cI am keeping something very precious alive by speaking my language\u201d \u2022 Health outcomes: \u201cit was [\u2026] the language that... pushed me to sobriety, [\u2026] it gave me... a sense of who I was\u201d. Although studies have reported protective effects of Indigenous language use on health (Ball & Moselle, 2013; Hallett, Chandler, & Lalonde, 2007; McIvor, 2013), health-related outcomes of language revitalization efforts, including MAP, remain underexplored (Whalen, Moss, & Baldwin, 2016). In addition to discussing the six exploratory themes from our study, our paper also proposes that these themes can inform future research in the area of language revitalization and wellbeing. References Ball, J., & Moselle, K. (2013). Contributions of culture and language in aboriginal head start in urban and northern communities to children\u2019s health outcomes: A review of theory and research. Prepared for Division of Children, Seniors & Healthy Development, Health Promotion and Chronic Disease Prevention Branch. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77\u2013101. http://doi.org/10.1191/1478088706qp063oa Braun, V., & Clarke, V. (2014). What can \u201cthematic analysis\u201d offer health and wellbeing researchers? International Journal of Qualitative Studies on Health and Well-Being, 9, 26152. http://doi.org/10.3402/qhw.v9.26152 Hallett, D., Chandler, M. J., & Lalonde, C. E. (2007). Aboriginal language knowledge and youth suicide. Cognitive Development, 22(3), 392\u2013399. http://doi.org/10.1016/j.cogdev.2007.02.001 Hinton, L. (2001). The master-apprentice language learning program. In L. Hinton & K. Hale (Eds.), The green book of language revitalization in practice (pp. 217\u2013226). San Diego, CA: Academic Press. McIvor, O. (2013). Protective effects of language learning, use and culture on the health and well-being of Indigenous people in Canada. In Proceedings of the 17th FEL Conference, FEL XVII: Endangered Languages Beyond Boundaries: Community Connections, Collaborative Approaches and Cross-Disciplinary Research (pp. 123\u2013131). Foundation for Endangered Languages in association with the University of Carleton. Whalen, D. H., Moss, M., & Baldwin, D. (2016). Healing through language: Positive physical health effects of indigenous language use. F1000Research, 5(852). http://doi.org/10.12688/f1000research.8656.1", + "citation_count": 182679, + "topics": [ + "Community Health and Development", + "Evaluation and Performance Assessment", + "Cognitive and psychological constructs research" + ], + "discovered_via": [ + "neighbor:openalex:W4390315357", + "neighbor:openalex:W4392182430", + "neighbor:openalex:W4394591980" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1791587663", + "title": "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology", + "year": 1989, + "url": "https://doi.org/10.2307/249008", + "doi": "https://doi.org/10.2307/249008", + "abstract": "Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions for these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and streamlined, resulting in two six-item scales with reliabilities of .98 for usefulness and .94 for ease of use. The scales exhibited high convergent, discriminant, and factorial validity. Perceived usefulness was significantly correlated with both self-reported current usage (r=.63, Study 1) and self-predicted future usage (r=.85, Study 2). Perceived ease of use was also significantly correlated with current usage (r=.45, Study 1) and future usage (r=.59, Study 2). In both studies, usefulness had a significantly greater correlation with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecedent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.", + "citation_count": 64381, + "topics": [ + "Technology Adoption and User Behaviour", + "Knowledge Management and Sharing", + "Digital Marketing and Social Media" + ], + "discovered_via": [ + "neighbor:openalex:W4383913712", + "neighbor:openalex:W4390880618" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1861492603", + "title": "Microsoft COCO: Common Objects in Context", + "year": 2014, + "url": "https://doi.org/10.1007/978-3-319-10602-1_48", + "doi": "https://doi.org/10.1007/978-3-319-10602-1_48", + "abstract": "", + "citation_count": 42169, + "topics": [ + "Advanced Neural Network Applications", + "Advanced Image and Video Retrieval Techniques", + "Multimodal Machine Learning Applications" + ], + "discovered_via": [ + "neighbor:openalex:W4386142022" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1665332082", + "title": "Principles and Practice of Structural Equation Modeling", + "year": 2005, + "url": "http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013016441&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA", + "doi": null, + "abstract": "Emphasizing concepts and rationale over mathematical minutiae, this is the most widely used, complete, and accessible structural equation modeling (SEM) text. Continuing the tradition of using real data examples from a variety of disciplines, the significantly revised fourth edition incorporates recent developments such as Pearl's graphing theory and the structural causal model (SCM), measurement invariance, and more. Readers gain a comprehensive understanding of all phases of SEM, from data collection and screening to the interpretation and reporting of the results. Learning is enhanced by exercises with answers, rules to remember, and topic boxes. The companion website supplies data, syntax, and output for the book's examples--now including files for Amos, EQS, LISREL, Mplus, Stata, and R (lavaan). New to This Edition \u00e2\u0080\u00a2Extensively revised to cover important new topics: Pearl's graphing theory and the SCM, causal inference frameworks, conditional process modeling, path models for longitudinal data, item response theory, and more. \u00e2\u0080\u00a2Chapters on best practices in all stages of SEM, measurement invariance in confirmatory factor analysis, and significance testing issues and bootstrapping. \u00e2\u0080\u00a2Expanded coverage of psychometrics. \u00e2\u0080\u00a2Additional computer tools: online files for all detailed examples, previously provided in EQS, LISREL, and Mplus, are now also given in Amos, Stata, and R (lavaan). \u00e2\u0080\u00a2Reorganized to cover the specification, identification, and analysis of observed variable models separately from latent variable models. Pedagogical Features \u00e2\u0080\u00a2Exercises with answers, plus end-of-chapter annotated lists of further reading. \u00e2\u0080\u00a2Real examples of troublesome data, demonstrating how to handle typical problems in analyses. \u00e2\u0080\u00a2Topic boxes on specialized issues, such as causes of nonpositive definite correlations. \u00e2\u0080\u00a2Boxed rules to remember. \u00e2\u0080\u00a2Website promoting a learn-by-doing approach, including syntax and data files for six widely used SEM computer tools.", + "citation_count": 36221, + "topics": [ + "Psychometric Methodologies and Testing", + "Educational and Psychological Assessments", + "Mental Health Research Topics" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1678356000", + "title": "Greedy function approximation: A gradient boosting machine.", + "year": 2001, + "url": "https://doi.org/10.1214/aos/1013203451", + "doi": "https://doi.org/10.1214/aos/1013203451", + "abstract": "Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest-descent minimization. A general gradient descent \u201cboosting\u201d paradigm is developed for additive expansions based on any fitting criterion.Specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification. Special enhancements are derived for the particular case where the individual additive components are regression trees, and tools for interpreting such \u201cTreeBoost\u201d models are presented. Gradient boosting of regression trees produces competitive, highly robust, interpretable procedures for both regression and classification, especially appropriate for mining less than clean data. Connections between this approach and the boosting methods of Freund and Shapire and Friedman, Hastie and Tibshirani are discussed.", + "citation_count": 28893, + "topics": [ + "Neural Networks and Applications", + "Machine Learning and Algorithms", + "Model Reduction and Neural Networks" + ], + "discovered_via": [ + "neighbor:openalex:W4386142022" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1815596065", + "title": "Confirmatory factor analysis for applied research", + "year": 2007, + "url": "https://doi.org/10.5860/choice.44-2769", + "doi": "https://doi.org/10.5860/choice.44-2769", + "abstract": "With its emphasis on practical and conceptual aspects, rather than mathematics or formulas, this accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA). Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology. The text shows how to formulate, program, and interpret CFA models using popular latent variable software packages (LISREL, Mplus, EQS, SAS/CALIS); understand the similarities and differences between CFA and exploratory factor analysis (EFA); and report results from a CFA study. It is filled with useful advice and tables that outline the procedures. The companion website offers data and program syntax files for most of the research examples, as well as links to CFA-related resources. New to This Edition *Updated throughout to incorporate important developments in latent variable modeling. *Chapter on Bayesian CFA and multilevel measurement models. *Addresses new topics (with examples): exploratory structural equation modeling, bifactor analysis, measurement invariance evaluation with categorical indicators, and a new method for scaling latent variables. *Utilizes the latest versions of major latent variable software packages--", + "citation_count": 15828, + "topics": [ + "Mental Health Research Topics" + ], + "discovered_via": [ + "neighbor:openalex:W4409825334" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1572092804", + "title": "Qualitative research & evaluation methods", + "year": 2002, + "url": "http://ci.nii.ac.jp/ncid/BA55243300", + "doi": null, + "abstract": "This book explains clearly conceptual issues and themes on qualitative research and evaluaton methods including: qualitative data, triangulated inquiry, qualitative inquiry, constructivism, constructionism, Complexity (chaos) theory, qualitative designs and data collection, fieldwork strategies, interviewing, tape-recording, ethical issues, analysis, interpretation and reporting, observations vs. perceived impacts and utilisation-focused evaluation reporting.", + "citation_count": 15257, + "topics": [ + "Evaluation and Performance Assessment" + ], + "discovered_via": [ + "neighbor:openalex:W4390315357" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2282821441", + "title": "\"Why Should I Trust You?\"", + "year": 2016, + "url": "https://doi.org/10.1145/2939672.2939778", + "doi": "https://doi.org/10.1145/2939672.2939778", + "abstract": "Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one.", + "citation_count": 15187, + "topics": [ + "Explainable Artificial Intelligence (XAI)", + "Adversarial Robustness in Machine Learning", + "Machine Learning in Healthcare" + ], + "discovered_via": [ + "neighbor:openalex:W4381848566", + "neighbor:openalex:W4400303336" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2755950973", + "title": "bibliometrix : An R-tool for comprehensive science mapping analysis", + "year": 2017, + "url": "https://doi.org/10.1016/j.joi.2017.08.007", + "doi": "https://doi.org/10.1016/j.joi.2017.08.007", + "abstract": "", + "citation_count": 14236, + "topics": [ + "Data Analysis with R", + "Scientific Computing and Data Management", + "Data Visualization and Analytics" + ], + "discovered_via": [ + "neighbor:openalex:W4316038168" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1821462560", + "title": "Distilling the Knowledge in a Neural Network", + "year": 2015, + "url": "https://doi.org/10.48550/arxiv.1503.02531", + "doi": "https://doi.org/10.48550/arxiv.1503.02531", + "abstract": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel.", + "citation_count": 13962, + "topics": [ + "Neural Networks and Applications", + "Time Series Analysis and Forecasting", + "Topic Modeling" + ], + "discovered_via": [ + "neighbor:openalex:W4381848566" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1981457167", + "title": "Data Analysis Using Regression and Multilevel/Hierarchical Models", + "year": 2006, + "url": "https://doi.org/10.1017/cbo9780511790942", + "doi": "https://doi.org/10.1017/cbo9780511790942", + "abstract": "Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.", + "citation_count": 13916, + "topics": [ + "Statistical Methods and Applications", + "Statistical Methods in Epidemiology", + "Advanced Statistical Modeling Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4401533174" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1730782591", + "title": "Experimental and quasi-experimental designs for generalized causal inference", + "year": 2002, + "url": "http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009884217&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA", + "doi": null, + "abstract": "1. Experiments and Generalized Causal Inference 2. Statistical Conclusion Validity and Internal Validity 3. Construct Validity and External Validity 4. Quasi-Experimental Designs That Either Lack a Control Group or Lack Pretest Observations on the Outcome 5. Quasi-Experimental Designs That Use Both Control Groups and Pretests 6. Quasi-Experimentation: Interrupted Time Series Designs 7. Regression Discontinuity Designs 8. Randomized Experiments: Rationale, Designs, and Conditions Conducive to Doing Them 9. Practical Problems 1: Ethics, Participant Recruitment, and Random Assignment 10. Practical Problems 2: Treatment Implementation and Attrition 11. Generalized Causal Inference: A Grounded Theory 12. Generalized Causal Inference: Methods for Single Studies 13. Generalized Causal Inference: Methods for Multiple Studies 14. A Critical Assessment of Our Assumptions", + "citation_count": 13410, + "topics": [ + "Qualitative Research Methods and Applications", + "Optimal Experimental Design Methods", + "Advanced Statistical Modeling Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4409825334" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2082704080", + "title": "UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age", + "year": 2015, + "url": "https://doi.org/10.1371/journal.pmed.1001779", + "doi": "https://doi.org/10.1371/journal.pmed.1001779", + "abstract": "Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.", + "citation_count": 13289, + "topics": [ + "Genetic Associations and Epidemiology", + "Nutrition, Genetics, and Disease", + "Health, Environment, Cognitive Aging" + ], + "discovered_via": [ + "neighbor:openalex:W4407173730" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2098118776", + "title": "Review : Knowledge Management and Knowledge Management Systems: Conceptual Foundations And Research Issues1,2", + "year": 2001, + "url": "https://doi.org/10.2307/3250961", + "doi": "https://doi.org/10.2307/3250961", + "abstract": "Knowledge is a broad and abstract notion that has defined epistemological debate in western philosophy since the classical Greek era. In the past few years, however, there has been a growing interest in treating knowledge as a significant organizational resource. Consistent with the interest in organizational knowledge and knowledge management (KM), IS researchers have begun promoting a class of information systems, referred to as knowledge management systems (KMS). The objective of KMS is to support creation, transfer, and application of knowledge in organizations. Knowledge and knowledge management are complex and multi-faceted concepts. Thus, effective development and implementation of KMS requires a foundation in several rich literatures. To be credible, KMS research and development should preserve and build upon the significant literature that exists in different but related fields. This paper provides a review and interpretation of knowledge management literatures in different fields with an eye toward identifying the important areas for research. We present a detailed process view of organizational knowledge management with a focus on the potential role of information technology in this process. Drawing upon the literature review and analysis of knowledge management processes, we discuss several important research issues surrounding the knowledge management processes and the role of IT in support of these processes.", + "citation_count": 9942, + "topics": [ + "Competitive and Knowledge Intelligence", + "Big Data and Business Intelligence", + "Knowledge Management and Sharing" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.0, + "relevance_reason": "General knowledge-management systems review with no hiring application and outside the publication window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W64581525", + "title": "Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory, and Antiracist Politics [1989]", + "year": 2018, + "url": "https://doi.org/10.4324/9780429500480-5", + "doi": "https://doi.org/10.4324/9780429500480-5", + "abstract": "This chapter develops a Black feminist criticism because it sets forth a problematic consequence of the tendency to treat race and gender as mutually exclusive categories of experience and analysis. It examines how this tendency is perpetuated by a single-axis framework that is dominant in antidiscrimination law and that is also reflected in feminist theory and antiracist politics. The chapter suggests that the single-axis framework erases Black women in the conceptualization, identification and remediation of race and sex discrimination by limiting inquiry to the experiences of otherwise-privileged members of the group. DeGraffenreid, Moore and Travenol are doctrinal manifestations of a common political and theoretical approach to discrimination which operates to marginalize Black women. The value of feminist theory to Black women is diminished because it evolves from a white racial context that is seldom acknowledged.", + "citation_count": 9525, + "topics": [ + "Gender Politics and Representation" + ], + "discovered_via": [ + "neighbor:openalex:W4394590365" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2908201961", + "title": "High-performance medicine: the convergence of human and artificial intelligence", + "year": 2018, + "url": "https://doi.org/10.1038/s41591-018-0300-7", + "doi": "https://doi.org/10.1038/s41591-018-0300-7", + "abstract": "", + "citation_count": 8593, + "topics": [ + "Artificial Intelligence in Healthcare and Education", + "Machine Learning in Healthcare", + "Radiomics and Machine Learning in Medical Imaging" + ], + "discovered_via": [ + "neighbor:openalex:W4404254054" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1834627138", + "title": "Deep Learning Face Attributes in the Wild", + "year": 2015, + "url": "https://doi.org/10.1109/iccv.2015.425", + "doi": "https://doi.org/10.1109/iccv.2015.425", + "abstract": "Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts.", + "citation_count": 7683, + "topics": [ + "Face recognition and analysis", + "Face and Expression Recognition", + "Biometric Identification and Security" + ], + "discovered_via": [ + "neighbor:openalex:W4316038168", + "neighbor:openalex:W4387789492" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1731081199", + "title": "Domain-Adversarial Training of Neural Networks", + "year": 2017, + "url": "https://doi.org/10.1007/978-3-319-58347-1_10", + "doi": "https://doi.org/10.1007/978-3-319-58347-1_10", + "abstract": "", + "citation_count": 7599, + "topics": [ + "Domain Adaptation and Few-Shot Learning", + "Video Surveillance and Tracking Methods", + "Gait Recognition and Analysis" + ], + "discovered_via": [ + "neighbor:openalex:W4387789492" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1539896512", + "title": "The Elaboration Likelihood Model of Persuasion", + "year": 1986, + "url": "https://doi.org/10.1016/s0065-2601(08)60214-2", + "doi": "https://doi.org/10.1016/s0065-2601(08)60214-2", + "abstract": "", + "citation_count": 7553, + "topics": [ + "Social and Intergroup Psychology", + "Behavioral Health and Interventions", + "Consumer Behavior in Brand Consumption and Identification" + ], + "discovered_via": [ + "neighbor:openalex:W4407762039" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2003490123", + "title": "Social Relationships and Mortality Risk: A Meta-analytic Review", + "year": 2010, + "url": "https://doi.org/10.1371/journal.pmed.1000316", + "doi": "https://doi.org/10.1371/journal.pmed.1000316", + "abstract": "BACKGROUND: The quality and quantity of individuals' social relationships has been linked not only to mental health but also to both morbidity and mortality. OBJECTIVES: This meta-analytic review was conducted to determine the extent to which social relationships influence risk for mortality, which aspects of social relationships are most highly predictive, and which factors may moderate the risk. DATA EXTRACTION: Data were extracted on several participant characteristics, including cause of mortality, initial health status, and pre-existing health conditions, as well as on study characteristics, including length of follow-up and type of assessment of social relationships. RESULTS: Across 148 studies (308,849 participants), the random effects weighted average effect size was OR = 1.50 (95% CI 1.42 to 1.59), indicating a 50% increased likelihood of survival for participants with stronger social relationships. This finding remained consistent across age, sex, initial health status, cause of death, and follow-up period. Significant differences were found across the type of social measurement evaluated (p<0.001); the association was strongest for complex measures of social integration (OR = 1.91; 95% CI 1.63 to 2.23) and lowest for binary indicators of residential status (living alone versus with others) (OR = 1.19; 95% CI 0.99 to 1.44). CONCLUSIONS: The influence of social relationships on risk for mortality is comparable with well-established risk factors for mortality. Please see later in the article for the Editors' Summary.", + "citation_count": 7160, + "topics": [ + "Health disparities and outcomes", + "Cardiac Health and Mental Health", + "Employment and Welfare Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4399518864" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1610612296", + "title": "Applied Missing Data Analysis", + "year": 2010, + "url": "http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020418619&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA", + "doi": null, + "abstract": "Part 1. An Introduction to Missing Data. 1.1 Introduction. 1.2 Chapter Overview. 1.3 Missing Data Patterns. 1.4 A Conceptual Overview of Missing Data heory. 1.5 A More Formal Description of Missing Data Theory. 1.6 Why Is the Missing Data Mechanism Important? 1.7 How Plausible Is the Missing at Random Mechanism? 1.8 An Inclusive Analysis Strategy. 1.9 Testing the Missing Completely at Random Mechanism. 1.10 Planned Missing Data Designs. 1.11 The Three-Form Design. 1.12 Planned Missing Data for Longitudinal Designs. 1.13 Conducting Power Analyses for Planned Missing Data Designs. 1.14 Data Analysis Example. 1.15 Summary. 1.16 Recommended Readings. Part 2. Traditional Methods for Dealing with Missing Data. 2.1 Chapter Overview. 2.2 An Overview of Deletion Methods. 2.3 Listwise Deletion. 2.4 Pairwise Deletion. 2.5 An Overview of Single Imputation Techniques. 2.6 Arithmetic Mean Imputation. 2.7 Regression Imputation. 2.8 Stochastic Regression Imputation. 2.9 Hot-Deck Imputation. 2.10 Similar Response Pattern Imputation. 2.11 Averaging the Available Items. 2.12 Last Observation Carried Forward. 2.13 An Illustrative Simulation Study. 2.14 Summary. 2.15 Recommended Readings. Part 3. An Introduction to Maximum Likelihood Estimation. 3.1 Chapter Overview. 3.2 The Univariate Normal Distribution. 3.3 The Sample Likelihood. 3.4 The Log-Likelihood. 3.5 Estimating Unknown Parameters. 3.6 The Role of First Derivatives. 3.7 Estimating Standard Errors. 3.8 Maximum Likelihood Estimation with Multivariate Normal Data. 3.9 A Bivariate Analysis Example. 3.10 Iterative Optimization Algorithms. 3.11 Significance Testing Using the Wald Statistic. 3.12 The Likelihood Ratio Test Statistic. 3.13 Should I Use the Wald Test or the Likelihood Ratio Statistic? 3.14 Data Analysis Example 1. 3.15 Data Analysis Example 2. 3.16 Summary. 3.17 Recommended Readings. Part 4. Maximum Likelihood Missing Data Handling. 4.1 Chapter Overview. 4.2 The Missing Data Log-Likelihood. 4.3 How Do the Incomplete Data Records Improve Estimation? 4.4 An Illustrative Computer Simulation Study. 4.5 Estimating Standard Errors with Missing Data. 4.6 Observed Versus Expected Information. 4.7 A Bivariate Analysis Example. 4.8 An Illustrative Computer Simulation Study. 4.9 An Overview of the EM Algorithm. 4.10 A Detailed Description of the EM Algorithm. 4.11 A Bivariate Analysis Example. 4.12 Extending EM to Multivariate Data. 4.13 Maximum Likelihood Software Options. 4.14 Data Analysis Example 1. 4.15 Data Analysis Example 2. 4.16 Data Analysis Example 3. 4.17 Data Analysis Example 4. 4.18 Data Analysis Example 5. 4.19 Summary. 4.20 Recommended Readings. Part 5. Improving the Accuracy of Maximum Likelihood Analyses. 5.1 Chapter Overview. 5.2 The Rationale for an Inclusive Analysis Strategy. 5.3 An Illustrative Computer Simulation Study. 5.4 Identifying a Set of Auxiliary Variables. 5.5 Incorporating Auxiliary Variables Into a Maximum Likelihood Analysis. 5.6 The Saturated Correlates Model. 5.7 The Impact of Non-Normal Data. 5.8 Robust Standard Errors. 5.9 Bootstrap Standard Errors. 5.10 The Rescaled Likelihood Ratio Test. 5.11 Bootstrapping the Likelihood Ratio Statistic. 5.12 Data Analysis Example 1. 5.13 Data Analysis Example 2. 5.14 Data Analysis Example 3. 5.15 Summary. 5.16 Recommended Readings. Part 6. An Introduction to Bayesian Estimation. 6.1 Chapter Overview. 6.2 What Makes Bayesian Statistics Different? 6.3 A Conceptual Overview of Bayesian Estimation. 6.4 Bayes' Theorem. 6.5 An Analysis Example. 6.6 How Does Bayesian Estimation Apply to Multiple Imputation? 6.7 The Posterior Distribution of the Mean. 6.8 The Posterior Distribution of the Variance. 6.9 The Posterior Distribution of a Covariance Matrix. 6.10 Summary. 6.11 Recommended Readings. Part 7. The Imputation Phase of Multiple Imputation. 7.1 Chapter Overview. 7.2 A Conceptual Description of the Imputation Phase. 7.3 A Bayesian Description of the Imputation Phase. 7.4 A Bivariate Analysis Example. 7.5 Data Augmentation with Multivariate Data. 7.6 Selecting Variables for Imputation. 7.7 The Meaning of Convergence. 7.8 Convergence Diagnostics. 7.9 Time-Series Plots. 7.10 Autocorrelation Function Plots. 7.11 Assessing Convergence from Alternate Starting Values. 7.12 Convergence Problems. 7.13 Generating the Final Set of Imputations. 7.14 How Many Data Sets Are Needed? 7.15 Summary. 7.16 Recommended Readings. Part 8. The Analysis and Pooling Phases of Multiple Imputation. 8.1 Chapter Overview. 8.2 The Analysis Phase. 8.3 Combining Parameter Estimates in the Pooling Phase. 8.4 Transforming Parameter Estimates Prior to Combining. 8.5 Pooling Standard Errors. 8.6 The Fraction of Missing Information and the Relative Increase in Variance. 8.7 When Is Multiple Imputation Comparable to Maximum Likelihood? 8.8 An Illustrative Computer Simulation Study. 8.9 Significance Testing Using the t Statistic. 8.10 An Overview of Multiparameter Significance Tests. 8.11 Testing Multiple Parameters Using the D1 Statistic. 8.12 Testing Multiple Parameters by Combining Wald Tests. 8.13 Testing Multiple Parameters by Combining Likelihood Ratio Statistics. 8.14 Data Analysis Example 1. 8.15 Data Analysis Example 2. 8.16 Data Analysis Example 3. 8.17 Summary. 8.18 Recommended Readings. Part 9. Practical Issues in Multiple Imputation. 9.1 Chapter Overview. 9.2 Dealing with Convergence Problems. 9.3 Dealing with Non-Normal Data. 9.4 To Round or Not to Round? 9.5 Preserving Interaction Effects. 9.6 Imputing Multiple-Item Questionnaires. 9.7 Alternate Imputation Algorithms. 9.8 Multiple Imputation Software Options. 9.9 Data Analysis Example 1. 9.10 Data Analysis Example 2. 9.11 Summary. 9.12 Recommended Readings. Part 10. Models for Missing Not at Random Data. 10.1 Chapter Overview. 10.2 An Ad Hoc Approach to Dealing with MNAR Data. 10.3 The Theoretical Rationale for MNAR Models. 10.4 The Classic Selection Model. 10.5 Estimating the Selection Model. 10.6 Limitations of the Selection Model. 10.7 An Illustrative Analysis. 10.8 The Pattern Mixture Model. 10.9 Limitations of the Pattern Mixture Model. 10.10 An Overview of the Longitudinal Growth Model. 10.11 A Longitudinal Selection Model. 10.12 Random Coefficient Selection Models. 10.13 Pattern Mixture Models for Longitudinal Analyses. 10.14 Identification Strategies for Longitudinal Pattern Mixture Models. 10.15 Delta Method Standard Errors. 10.16 Overview of the Data Analysis Examples. 10.17 Data Analysis Example 1. 10.18 Data Analysis Example 2. 10.19 Data Analysis Example 3. 10.20 Data Analysis Example 4. 10.21 Summary. 10.22 Recommended Readings. Part 11. Wrapping Things Up: Some Final Practical Considerations. 11.1 Chapter Overview. 11.2 Maximum Likelihood Software Options. 11.3 Multiple Imputation Software Options. 11.4 Choosing between Maximum Likelihood and Multiple Imputation. 11.5 Reporting the Results from a Missing Data Analysis. 11.6 Final Thoughts. 11.7 Recommended Readings.", + "citation_count": 6890, + "topics": [ + "Bayesian Methods and Mixture Models", + "Statistical Methods and Bayesian Inference" + ], + "discovered_via": [ + "neighbor:openalex:W4409825334" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W429766147", + "title": "Applied Predictive Modeling", + "year": 2013, + "url": "https://doi.org/10.1007/978-1-4614-6849-3", + "doi": "https://doi.org/10.1007/978-1-4614-6849-3", + "abstract": "", + "citation_count": 6628, + "topics": [ + "Statistical Methods and Bayesian Inference", + "Diverse Scientific and Engineering Research", + "Statistics Education and Methodologies" + ], + "discovered_via": [ + "neighbor:openalex:W4385952870" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1986792585", + "title": "The Complexity of Intersectionality", + "year": 2005, + "url": "https://doi.org/10.1086/426800", + "doi": "https://doi.org/10.1086/426800", + "abstract": "", + "citation_count": 6468, + "topics": [ + "Early Childhood Education and Development", + "Social and Educational Sciences" + ], + "discovered_via": [ + "neighbor:openalex:W4366989571" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1980569376", + "title": "Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1", + "year": 1992, + "url": "https://doi.org/10.1111/j.1559-1816.1992.tb00945.x", + "doi": "https://doi.org/10.1111/j.1559-1816.1992.tb00945.x", + "abstract": "Previous research indicates that perceived usefulness is a major determinant and predictor of intentions to use computers in the workplace. In contrast, the impact of enjoyment on usage intentions has not been examined. Two studies are reported concerning the relative effects of usefulness and enjoyment on intentions to use, and usage of, computers in the workplace. Usefulness had a strong effect on usage intentions in both Study 1, regarding word processing software (\u03b2=.68), and Study 2, regarding business graphics programs (\u03b2=.79). As hypothesized, enjoyment also had a significant effect on intentions in both studies, controlling for perceived usefulness (\u03b2=.16 and 0.15 for Studies 1 and 2, respectively). Study 1 found that intentions correlated 0.63 with system usage and that usefulness and enjoyment influenced usage behavior entirely indirectly through their effects on intentions. In both studies, a positive interaction between usefulness and enjoyment was observed. Together, usefulness and enjoyment explained 62% (Study 1) and 75% (Study 2) of the variance in usage intentions. Moreover, usefulness and enjoyment were found to mediate fully the effects on usage intentions of perceived output quality and perceived ease of use. As hypothesized, a measure of task importance moderated the effects of ease of use and output quality on usefulness but not on enjoyment. Several implications are drawn for how to design computer programs to be both more useful and more enjoyable in order to increase their acceptability among potential users.", + "citation_count": 6135, + "topics": [ + "Technology Adoption and User Behaviour", + "Digital Marketing and Social Media", + "Knowledge Management and Sharing" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2473418344", + "title": "Deep Learning with Differential Privacy", + "year": 2016, + "url": "https://doi.org/10.1145/2976749.2978318", + "doi": "https://doi.org/10.1145/2976749.2978318", + "abstract": "Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information. The models should not expose private information in these datasets. Addressing this goal, we develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy. Our implementation and experiments demonstrate that we can train deep neural networks with non-convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.", + "citation_count": 5859, + "topics": [ + "Privacy-Preserving Technologies in Data", + "Adversarial Robustness in Machine Learning", + "Stochastic Gradient Optimization Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4381848566" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2891503716", + "title": "Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)", + "year": 2018, + "url": "https://doi.org/10.1109/access.2018.2870052", + "doi": "https://doi.org/10.1109/access.2018.2870052", + "abstract": "At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black-box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has triggered a new debate on explainable AI (XAI). A research field holds substantial promise for improving trust and transparency of AI-based systems. It is recognized as the sine qua non for AI to continue making steady progress without disruption. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI. Through the lens of the literature, we review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories.", + "citation_count": 5814, + "topics": [ + "Explainable Artificial Intelligence (XAI)", + "Adversarial Robustness in Machine Learning", + "Artificial Intelligence in Healthcare and Education" + ], + "discovered_via": [ + "neighbor:openalex:W4404254054" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W179875071", + "title": "Recurrent neural network based language model", + "year": 2010, + "url": "https://doi.org/10.21437/interspeech.2010-343", + "doi": "https://doi.org/10.21437/interspeech.2010-343", + "abstract": "A new recurrent neural network based language model (RNN LM) with applications to speech recognition is presented. Results indicate that it is possible to obtain around 50% reduction of perplexity by using mixture of several RNN LMs, compared to a state of the art backoff language model. Speech recognition experiments show around 18% reduction of word error rate on the Wall Street Journal task when comparing models trained on the same amount of data, and around 5% on the much harder NIST RT05 task, even when the backoff model is trained on much more data than the RNN LM. We provide ample empirical evidence to suggest that connectionist language models are superior to standard n-gram techniques, except their high computational (training) complexity. Index Terms: language modeling, recurrent neural networks, speech recognition", + "citation_count": 5420, + "topics": [ + "Speech Recognition and Synthesis", + "Natural Language Processing Techniques", + "Topic Modeling" + ], + "discovered_via": [ + "neighbor:openalex:W4391830676" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1575299154", + "title": "Journal of the American Statistical Association", + "year": 1995, + "url": "https://doi.org/10.1016/0169-2070(95)90067-5", + "doi": "https://doi.org/10.1016/0169-2070(95)90067-5", + "abstract": "", + "citation_count": 5364, + "topics": [ + "Consumer Market Behavior and Pricing", + "Innovation Diffusion and Forecasting", + "Supply Chain and Inventory Management" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1496437928", + "title": "Introduction to Classical and Modern Test Theory", + "year": 1986, + "url": "http://lib.ui.ac.id/detail.jsp?id=20113013", + "doi": null, + "abstract": "", + "citation_count": 4811, + "topics": [ + "Educational Assessment and Pedagogy", + "Educational Technology and Assessment" + ], + "discovered_via": [ + "neighbor:openalex:W4409825334" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2083927153", + "title": "The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository", + "year": 2013, + "url": "https://doi.org/10.1007/s10278-013-9622-7", + "doi": "https://doi.org/10.1007/s10278-013-9622-7", + "abstract": "The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)-an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.", + "citation_count": 4602, + "topics": [ + "Digital Radiography and Breast Imaging", + "Scientific Computing and Data Management", + "Medical Imaging Techniques and Applications" + ], + "discovered_via": [ + "neighbor:openalex:W4407173730" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1631150390", + "title": "The second machine age: work, progress, and prosperity in a time of brilliant technologies", + "year": 2015, + "url": "https://doi.org/10.5860/choice.184834", + "doi": "https://doi.org/10.5860/choice.184834", + "abstract": "A New York Times Bestseller A revolution is under way. In recent years, Googles autonomous cars have logged thousands of miles on American highways and IBMs Watson trounced the best human Jeopardy! players. Digital technologieswith hardware, software, and networks at their corewill in the near future diagnose diseases more accurately than doctors can, apply enormous data sets to transform retailing, and accomplish many tasks once considered uniquely human. In The Second Machine Age MITs Erik Brynjolfsson and Andrew McAfeetwo thinkers at the forefront of their fieldreveal the forces driving the reinvention of our lives and our economy. As the full impact of digital technologies is felt, we will realize immense bounty in the form of dazzling personal technology, advanced infrastructure, and near-boundless access to the cultural items that enrich our lives. Amid this bounty will also be wrenching change. Professions of all kindsfrom lawyers to truck driverswill be forever upended. Companies will be forced to transform or die. Recent economic indicators reflect this shift: fewer people are working, and wages are falling even as productivity and profits soar. Drawing on years of research and up-to-the-minute trends, Brynjolfsson and McAfee identify the best strategies for survival and offer a new path to prosperity. These include revamping education so that it prepares people for the next economy instead of the last one, designing new collaborations that pair brute processing power with human ingenuity, and embracing policies that make sense in a radically transformed landscape. A fundamentally optimistic book, The Second Machine Age will alter how we think about issues of technological, societal, and economic progress.", + "citation_count": 4426, + "topics": [ + "Big Data Technologies and Applications", + "Big Data and Business Intelligence", + "ICT Impact and Policies" + ], + "discovered_via": [ + "neighbor:openalex:W4399518864" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1933349210", + "title": "VQA: Visual Question Answering", + "year": 2015, + "url": "https://doi.org/10.1109/iccv.2015.279", + "doi": "https://doi.org/10.1109/iccv.2015.279", + "abstract": "We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing ~0.25M images, ~0.76M questions, and ~10M answers (www.visualqa.org), and discuss the information it provides. Numerous baselines for VQA are provided and compared with human performance.", + "citation_count": 4287, + "topics": [ + "Multimodal Machine Learning Applications", + "Advanced Image and Video Retrieval Techniques", + "Domain Adaptation and Few-Shot Learning" + ], + "discovered_via": [ + "neighbor:openalex:W4386142022" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2261525379", + "title": "Beyond the hype: Big data concepts, methods, and analytics", + "year": 2014, + "url": "https://doi.org/10.1016/j.ijinfomgt.2014.10.007", + "doi": "https://doi.org/10.1016/j.ijinfomgt.2014.10.007", + "abstract": "Size is the first, and at times, the only dimension that leaps out at the mention of big data. This paper attempts to offer a broader definition of big data that captures its other unique and defining characteristics. The rapid evolution and adoption of big data by industry has leapfrogged the discourse to popular outlets, forcing the academic press to catch up. Academic journals in numerous disciplines, which will benefit from a relevant discussion of big data, have yet to cover the topic. This paper presents a consolidated description of big data by integrating definitions from practitioners and academics. The paper's primary focus is on the analytic methods used for big data. A particular distinguishing feature of this paper is its focus on analytics related to unstructured data, which constitute 95% of big data. This paper highlights the need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats. This paper also reinforces the need to devise new tools for predictive analytics for structured big data. The statistical methods in practice were devised to infer from sample data. The heterogeneity, noise, and the massive size of structured big data calls for developing computationally efficient algorithms that may avoid big data pitfalls, such as spurious correlation.", + "citation_count": 4121, + "topics": [ + "Big Data Technologies and Applications", + "Data Visualization and Analytics", + "Time Series Analysis and Forecasting" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2111143102", + "title": "Theory Construction in Qualitative Research", + "year": 2012, + "url": "https://doi.org/10.1177/0735275112457914", + "doi": "https://doi.org/10.1177/0735275112457914", + "abstract": "A critical pathway for conceptual innovation in the social is the construction of theoretical ideas based on empirical data. Grounded theory has become a leading approach promising the construction of novel theories. Yet grounded theory\u2013based theoretical innovation has been scarce in part because of its commitment to let theories emerge inductively rather than imposing analytic frameworks a priori. We note, along with a long philosophical tradition, that induction does not logically lead to novel theoretical insights. Drawing from the theory of inference, meaning, and action of pragmatist philosopher Charles S. Peirce, we argue that abduction, rather than induction, should be the guiding principle of empirically based theory construction. Abduction refers to a creative inferential process aimed at producing new hypotheses and theories based on surprising research evidence. We propose that abductive analysis arises from actors\u2019 social and intellectual positions but can be further aided by careful methodological data analysis. We outline how formal methodological steps enrich abductive analysis through the processes of revisiting, defamiliarization, and alternative casing.", + "citation_count": 3788, + "topics": [ + "Qualitative Research Methods and Applications", + "Contemporary Sociological Theory and Practice", + "Qualitative Research Methods and Ethics" + ], + "discovered_via": [ + "neighbor:openalex:W4386714740" + ], + "relevance_score": 0.0, + "relevance_reason": "A general qualitative theory-construction methods paper with no AI or hiring focus.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W1497226654", + "title": "Personal Computing: Toward a Conceptual Model of Utilization1", + "year": 1991, + "url": "https://doi.org/10.2307/249443", + "doi": "https://doi.org/10.2307/249443", + "abstract": "Organizations continue to invest heavily in personal computers for their knowledge workers. When use is optional, however, having access to the technology by no means ensures it will be used or used effectively. To help us gain a better understanding of factors that influence the use of personal computers, researchers have recently adapted the theory of reasoned action proposed by Fishbein and Azjen (1975). This study uses a competing theory of behavior proposed by Triandis (1980). Responses were collected from 212 knowledge workers in nine divisions of a multi-national firm, and the measures and research hypotheses were analyzed using partial least squares (PLS). The results show that social norms and three components of expected consequences (complexity of use, fit between the job and PC capabilities, and long-term consequences) have a strong influence on utilization. These findings confirm the importance of the expected consequences of using PC technology, suggesting that training programs and organizational policies could be instituted to enhance or modify these expectations.", + "citation_count": 3761, + "topics": [ + "Technology Adoption and User Behaviour", + "Knowledge Management and Sharing", + "Digital Marketing and Social Media" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1965631677", + "title": "The Increasing Dominance of Teams in Production of Knowledge", + "year": 2007, + "url": "https://doi.org/10.1126/science.1136099", + "doi": "https://doi.org/10.1126/science.1136099", + "abstract": "We have used 19.9 million papers over 5 decades and 2.1 million patents to demonstrate that teams increasingly dominate solo authors in the production of knowledge. Research is increasingly done in teams across nearly all fields. Teams typically produce more frequently cited research than individuals do, and this advantage has been increasing over time. Teams now also produce the exceptionally high-impact research, even where that distinction was once the domain of solo authors. These results are detailed for sciences and engineering, social sciences, arts and humanities, and patents, suggesting that the process of knowledge creation has fundamentally changed.", + "citation_count": 3617, + "topics": [ + "Innovation and Knowledge Management", + "scientometrics and bibliometrics research", + "Open Source Software Innovations" + ], + "discovered_via": [ + "neighbor:openalex:W4401533174" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1662240627", + "title": "Attitudes and Persuasion: Classic and Contemporary Approaches", + "year": 1981, + "url": "http://ci.nii.ac.jp/ncid/BA28042055", + "doi": null, + "abstract": "Attitudes and Persuasion -- Foreword -- Preface -- Attitudes and Persuasion -- Introduction to Attitudes and Persuasion -- Conditioning and Modeling Approaches -- The Message-learning Approach -- Judgmental Approaches -- Motivational Approaches -- Attributional Approaches -- Combinatory Approaches -- Self-persuasion Approaches -- Epilog: A General Framework for Understanding Attitude Change Processes", + "citation_count": 3407, + "topics": [ + "Complex Systems and Decision Making", + "Social Media and Politics" + ], + "discovered_via": [ + "neighbor:openalex:W4407762039" + ], + "relevance_score": 0.0, + "relevance_reason": "General work on attitudes and persuasion with no AI or hiring content.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W1971765404", + "title": "What is an adequate sample size? Operationalising data saturation for theory-based interview studies", + "year": 2009, + "url": "https://doi.org/10.1080/08870440903194015", + "doi": "https://doi.org/10.1080/08870440903194015", + "abstract": "In interview studies, sample size is often justified by interviewing participants until reaching 'data saturation'. However, there is no agreed method of establishing this. We propose principles for deciding saturation in theory-based interview studies (where conceptual categories are pre-established by existing theory). First, specify a minimum sample size for initial analysis (initial analysis sample). Second, specify how many more interviews will be conducted without new ideas emerging (stopping criterion). We demonstrate these principles in two studies, based on the theory of planned behaviour, designed to identify three belief categories (Behavioural, Normative and Control), using an initial analysis sample of 10 and stopping criterion of 3. Study 1 (retrospective analysis of existing data) identified 84 shared beliefs of 14 general medical practitioners about managing patients with sore throat without prescribing antibiotics. The criterion for saturation was achieved for Normative beliefs but not for other beliefs or studywise saturation. In Study 2 (prospective analysis), 17 relatives of people with Paget's disease of the bone reported 44 shared beliefs about taking genetic testing. Studywise data saturation was achieved at interview 17. We propose specification of these principles for reporting data saturation in theory-based interview studies. The principles may be adaptable for other types of studies.", + "citation_count": 3392, + "topics": [ + "Behavioral Health and Interventions", + "Health Promotion and Cardiovascular Prevention", + "Advanced Causal Inference Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4383913712" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2040793419", + "title": "SOMETHING OLD, SOMETHING NEW: A LONGITUDINAL STUDY OF SEARCH BEHAVIOR AND NEW PRODUCT INTRODUCTION.", + "year": 2002, + "url": "https://doi.org/10.2307/3069433", + "doi": "https://doi.org/10.2307/3069433", + "abstract": "We examine how firms search, or solve problems, to create new products. According to organizational learning research, firms position themselves in a unidimensional search space that spans a spectrum from local to distant search. Our findings in the global robotics industry suggest that firms' search efforts actually vary across two distinct dimensions: search depth, or how frequently the firm reuses its existing knowledge, and search scope, or how widely the firm explores new knowledge.", + "citation_count": 3386, + "topics": [ + "Innovation and Knowledge Management", + "Business Strategy and Innovation", + "Open Source Software Innovations" + ], + "discovered_via": [ + "neighbor:openalex:W4401533174" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2475334473", + "title": "Wide & Deep Learning for Recommender Systems", + "year": 2016, + "url": "https://doi.org/10.1145/2988450.2988454", + "doi": "https://doi.org/10.1145/2988450.2988454", + "abstract": "Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort. With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features. However, deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank. In this paper, we present Wide & Deep learning---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems. We productionized and evaluated the system on Google Play, a commercial mobile app store with over one billion active users and over one million apps. Online experiment results show that Wide & Deep significantly increased app acquisitions compared with wide-only and deep-only models. We have also open-sourced our implementation in TensorFlow.", + "citation_count": 3334, + "topics": [ + "Recommender Systems and Techniques", + "Advanced Bandit Algorithms Research", + "Stochastic Gradient Optimization Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4316038168" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2169855821", + "title": "Review: Information Technology and Organizational Performance: An Integrative Model of IT Business Value1", + "year": 2004, + "url": "https://doi.org/10.2307/25148636", + "doi": "https://doi.org/10.2307/25148636", + "abstract": "Despite the importance to researchers, managers, and policy makers of how information technology (IT) contributes to organizational performance, there is uncertainty and debate about what we know and don\u2019t know. A review of the literature reveals that studies examining the association between information technology and organizational performance are divergent in how they conceptualize key constructs and their interrelationships. We develop a model of IT business value based on the resource-based view of the firm that integrates the various strands of research into a single framework. We apply the integrative model to synthesize what is known about IT business value and guide future research by developing propositions and suggesting a research agenda. A principal finding is that IT is valuable, but the extent and dimensions are dependent upon internal and external factors, including complementary organizational resources of the firm and its trading partners, as well as the competitive and macro environment. Our analysis provides a blueprint to guide future research and facilitate knowledge accumulation and creation concerning the organizational performance impacts of information technology.", + "citation_count": 3329, + "topics": [ + "Innovation and Knowledge Management", + "Information Technology Governance and Strategy", + "Business Strategy and Innovation" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1851740361", + "title": "Translation, adaptation and validation of instruments or scales for use in cross\u2010cultural health care research: a clear and user\u2010friendly guideline", + "year": 2010, + "url": "https://doi.org/10.1111/j.1365-2753.2010.01434.x", + "doi": "https://doi.org/10.1111/j.1365-2753.2010.01434.x", + "abstract": "RATIONALE, AIMS AND OBJECTIVES: The diversity of the population worldwide suggests a great need for cross-culturally validated research instruments or scales. Researchers and clinicians must have access to reliable and valid measures of concepts of interest in their own cultures and languages to conduct cross-cultural research and/or provide quality patient care. Although there are well-established methodological approaches for translating, adapting and validating instruments or scales for use in cross-cultural health care research, a great variation in the use of these approaches continues to prevail in the health care literature. Therefore, the objectives of this scholarly paper were to review published recommendations of cross-cultural validation of instruments and scales, and to propose and present a clear and user-friendly guideline for the translation, adaptation and validation of instruments or scales for cross-cultural health care research. METHODS: A review of highly recommended methodological approaches to translation, adaptation and cross-cultural validation of research instruments or scales was performed. Recommendations were summarized and incorporated into a seven-step guideline. Each one of the steps was described and key points were highlighted. Example of a project using the proposed steps of the guideline was fully described. CONCLUSIONS: Translation, adaptation and validation of instruments or scales for cross-cultural research is very time-consuming and requires careful planning and the adoption of rigorous methodological approaches to derive a reliable and valid measure of the concept of interest in the target population.", + "citation_count": 3197, + "topics": [ + "Cultural Competency in Health Care", + "Health Policy Implementation Science", + "Health Education and Validation" + ], + "discovered_via": [ + "neighbor:openalex:W4409825334" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1829634234", + "title": "Sampling in Interview-Based Qualitative Research: A Theoretical and Practical Guide", + "year": 2013, + "url": "https://doi.org/10.1080/14780887.2013.801543", + "doi": "https://doi.org/10.1080/14780887.2013.801543", + "abstract": "Sampling is central to the practice of qualitative methods, but compared with data collection and analysis its processes have been discussed relatively little. A four-point approach to sampling in qualitative interview-based research is presented and critically discussed in this article, which integrates theory and process for the following: (1) defining a sample universe, by way of specifying inclusion and exclusion criteria for potential participation; (2) deciding upon a sample size, through the conjoint consideration of epistemological and practical concerns; (3) selecting a sampling strategy, such as random sampling, convenience sampling, stratified sampling, cell sampling, quota sampling or a single-case selection strategy; and (4) sample sourcing, which includes matters of advertising, incentivising, avoidance of bias, and ethical concerns pertaining to informed consent. The extent to which these four concerns are met and made explicit in a qualitative study has implications for its coherence, transparency, impact and trustworthiness.", + "citation_count": 3135, + "topics": [ + "Qualitative Research Methods and Ethics", + "Ethics in Clinical Research", + "HIV, Drug Use, Sexual Risk" + ], + "discovered_via": [ + "neighbor:openalex:W4394591980" + ], + "relevance_score": 0.0, + "relevance_reason": "General qualitative sampling guidance with no AI-enabled hiring application.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W43120215", + "title": "User Centered System Design", + "year": 1986, + "url": "https://doi.org/10.1201/b15703", + "doi": "https://doi.org/10.1201/b15703", + "abstract": "", + "citation_count": 3088, + "topics": [ + "Persona Design and Applications" + ], + "discovered_via": [ + "neighbor:openalex:W4392182430" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1973294727", + "title": "Moral Consciousness and Communicative Action.", + "year": 1992, + "url": "https://doi.org/10.2307/2185962", + "doi": "https://doi.org/10.2307/2185962", + "abstract": "Introduction by Thomas McCarthy. Philosophy as Stand--In and Interpreter. Reconstruction and Interpretation in the Social Sciences. Discourse Ethics: Notes on a Program of Philosophical Justification. Moral Consciousness and Communicative Action. Morality and Ethical Life: Does Hegela s Critique of Kant Apply to Discourse Ethics?. Index.", + "citation_count": 3087, + "topics": [ + "Critical Theory and Philosophy" + ], + "discovered_via": [ + "neighbor:openalex:W4383913712" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1602519790", + "title": "The New Industrial Engineering: Information Technology and Business Process Redesign", + "year": 2011, + "url": "http://hdl.handle.net/1721.1/48613", + "doi": null, + "abstract": "\"Published in Sloan Management Review, Summer 1990, vol. 31, no. 4.\"--Added t.p.", + "citation_count": 3056, + "topics": [ + "Business Process Modeling and Analysis" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2111952268", + "title": "The Functional Approach to the Study of Attitudes", + "year": 1960, + "url": "https://doi.org/10.1086/266945", + "doi": "https://doi.org/10.1086/266945", + "abstract": "At the psychological level the reasons for holding or for changing attitudes are found in the functions they perform for the individual, specifically the functions of adjustment, ego defense, value expression, and knowledge. The conditions necessary to arouse or modify an attitude vary according to the motivational basis of the attitude. Ego-defensive attitudes, for example, can be aroused by threats, appeals to hatred and repressed impulses, and authoritarian suggestion, and can be changed by removal of threat, catharsis, and self-insight. Expressive attitudes are aroused by cues associated with the individual's values and by the need to reassert his self-image and can be changed by showing the appropriateness of the new or modified beliefs to the self-concept Brain washing is primarily directed at the value-expressive function and operates by controlling all environmental supports of old values. Changing attitudes may involve generalization of change to related areas of belief and feeling. Minimal generalization seems to be the rule among adults; for example, in politics voting for an opposition candidate does not have much effect upon party identification.", + "citation_count": 2921, + "topics": [ + "Opinion Dynamics and Social Influence", + "Social and Intergroup Psychology" + ], + "discovered_via": [ + "neighbor:openalex:W4407762039" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1526225581", + "title": "What Should Be Done with Equity Theory?", + "year": 1980, + "url": "https://doi.org/10.1007/978-1-4613-3087-5_2", + "doi": "https://doi.org/10.1007/978-1-4613-3087-5_2", + "abstract": "", + "citation_count": 2903, + "topics": [ + "Corruption and Economic Development", + "Experimental Behavioral Economics Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1819467863", + "title": "Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares", + "year": 2015, + "url": "https://doi.org/10.3758/s13428-015-0619-7", + "doi": "https://doi.org/10.3758/s13428-015-0619-7", + "abstract": "", + "citation_count": 2828, + "topics": [ + "Sensory Analysis and Statistical Methods", + "Advanced Statistical Modeling Techniques", + "Advanced Statistical Methods and Models" + ], + "discovered_via": [ + "neighbor:openalex:W4409825334" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1963547174", + "title": "The structure of ill structured problems", + "year": 1973, + "url": "https://doi.org/10.1016/0004-3702(73)90011-8", + "doi": "https://doi.org/10.1016/0004-3702(73)90011-8", + "abstract": "", + "citation_count": 2563, + "topics": [ + "AI in Service Interactions", + "Advanced Text Analysis Techniques", + "AI-based Problem Solving and Planning" + ], + "discovered_via": [ + "neighbor:openalex:W4401533174" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1593544550", + "title": "Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study1", + "year": 1999, + "url": "https://doi.org/10.2307/249749", + "doi": "https://doi.org/10.2307/249749", + "abstract": "A model, based on Bandura's Social Cognitive Theory, was developed to test the influence of computer self-efficacy, outcome expectations, affect, and anxiety on computer usage. The model was tested using longitudinal data gathered from 394 end users over a one-year interval. Significant relationships were found between computer self-efficacy and outcome expectations, and between self-efficacy and affect and anxiety and use. Performance outcomes were found to influence affect and use, while affect was significantly related to use. Overall, the findings provide strong confirmation that both self-efficacy and outcome expectations impact on an individual's affective and behavioral reactions to information technology.", + "citation_count": 2509, + "topics": [ + "Technology Adoption and User Behaviour", + "Digital Marketing and Social Media", + "Impact of Technology on Adolescents" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1978484347", + "title": "The productivity paradox of information technology", + "year": 1993, + "url": "https://doi.org/10.1145/163298.163309", + "doi": "https://doi.org/10.1145/163298.163309", + "abstract": "article Free Access Share on The productivity paradox of information technology Author: Erik Brynjolfsson View Profile Authors Info & Claims Communications of the ACMVolume 36Issue 12Dec. 1993pp 66\u201377https://doi.org/10.1145/163298.163309Published:01 December 1993Publication History 215citation25,352DownloadsMetricsTotal Citations215Total Downloads25,352Last 12 Months2,602Last 6 weeks235 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF", + "citation_count": 2479, + "topics": [ + "Intellectual Capital and Performance Analysis", + "Business Strategy and Innovation", + "Accounting and Organizational Management" + ], + "discovered_via": [ + "neighbor:openalex:W4383913712" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2134267121", + "title": "Getting a Job: Is There a Motherhood Penalty?", + "year": 2007, + "url": "https://doi.org/10.1086/511799", + "doi": "https://doi.org/10.1086/511799", + "abstract": "Survey research finds that mothers suffer a substantial wage penalty, although the causal mechanism producing it remains elusive. The authors employed a laboratory experiment to evaluate the hypothesis that status-based discrimination plays an important role and an audit study of actual employers to assess its real-world implications. In both studies, participants evaluated application materials for a pair of same-gender equally qualified job candidates who differed on parental status. The laboratory experiment found that mothers were penalized on a host of measures, including perceived competence and recommended starting salary. Men were not penalized for, and sometimes benefited from, being a parent. The audit study showed that actual employers discriminate against mothers, but not against fathers. Mothers experience disadvantages in the workplace in addition to those commonly associated with gender. For example, two recent studies find that employed mothers in the United States suffer a per-child wage penalty of approximately 5%, on average, after controlling for the usual human capital and occupational factors that affect wages (Budig and England 2001; Anderson, Binder, and Krause 2003). In a summary of economic research, Crittenden (2001) concludes that, for those under the age of 35, the pay gap between mothers and nonmothers is larger than the pay gap between men and women. As Glass (2004) notes, employed mothers are 1", + "citation_count": 2452, + "topics": [ + "Gender Diversity and Inequality", + "Work-Family Balance Challenges", + "Gender Politics and Representation" + ], + "discovered_via": [ + "neighbor:openalex:W4386714740" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1986904066", + "title": "Adaptation on Rugged Landscapes", + "year": 1997, + "url": "https://doi.org/10.1287/mnsc.43.7.934", + "doi": "https://doi.org/10.1287/mnsc.43.7.934", + "abstract": "A simple model is developed to explore the interrelationship between processes of organizational level change and population selection forces. A critical property of the model is that the effect on organizational fitness of the various attributes that constitute an organization's form is interactive. As a result of these interaction effects, the fitness landscape is \u201crugged.\u201d An organization's form at founding has a persistent effect on its future form when there are multiple peaks in the fitness landscape, since the particular peak that an organization discovers is influenced by its starting position in the space of alternative organizational forms. Selection pressures influence the distribution of the organizational forms that emerge from the process of local adaptation. The ability of established organizations to respond to changing environments is importantly conditioned by the extent to which elements of organizational form interact in their effect on organizational fitness. Tightly coupled organizations are subject to high rates of failure in changing environments. Furthermore, successful \u201creorientations\u201d are strongly associated with survival for tightly coupled organizations, but not for more loosely coupled organizations that are able to engage in effective local adaptation.", + "citation_count": 2263, + "topics": [ + "Evolutionary Game Theory and Cooperation", + "Complex Systems and Decision Making", + "Innovation and Knowledge Management" + ], + "discovered_via": [ + "neighbor:openalex:W4401533174" + ], + "relevance_score": 0.0, + "relevance_reason": "Models organizational adaptation and selection forces, not employee hiring or AI.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2145239266", + "title": "The Impact of E-Learning in Medical Education", + "year": 2006, + "url": "https://doi.org/10.1097/00001888-200603000-00002", + "doi": "https://doi.org/10.1097/00001888-200603000-00002", + "abstract": "The authors provide an introduction to e-learning and its role in medical education by outlining key terms, the components of e-learning, the evidence for its effectiveness, faculty development needs for implementation, evaluation strategies for e-learning and its technology, and how e-learning might be considered evidence of academic scholarship. E-learning is the use of Internet technologies to enhance knowledge and performance. E-learning technologies offer learners control over content, learning sequence, pace of learning, time, and often media, allowing them to tailor their experiences to meet their personal learning objectives. In diverse medical education contexts, e-learning appears to be at least as effective as traditional instructor-led methods such as lectures. Students do not see e-learning as replacing traditional instructor-led training but as a complement to it, forming part of a blended-learning strategy. A developing infrastructure to support e-learning within medical education includes repositories, or digital libraries, to manage access to e-learning materials, consensus on technical standardization, and methods for peer review of these resources. E-learning presents numerous research opportunities for faculty, along with continuing challenges for documenting scholarship. Innovations in e-learning technologies point toward a revolution in education, allowing learning to be individualized (adaptive learning), enhancing learners' interactions with others (collaborative learning), and transforming the role of the teacher. The integration of e-learning into medical education can catalyze the shift toward applying adult learning theory, where educators will no longer serve mainly as the distributors of content, but will become more involved as facilitators of learning and assessors of competency.", + "citation_count": 2236, + "topics": [ + "Online and Blended Learning", + "Innovative Teaching and Learning Methods", + "Innovations in Medical Education" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2086543716", + "title": "Breast imaging reporting and data system (BI-RADS)", + "year": 2002, + "url": "https://doi.org/10.1016/s0033-8389(01)00017-3", + "doi": "https://doi.org/10.1016/s0033-8389(01)00017-3", + "abstract": "", + "citation_count": 2157, + "topics": [ + "Breast Lesions and Carcinomas", + "MRI in cancer diagnosis", + "Global Cancer Incidence and Screening" + ], + "discovered_via": [ + "neighbor:openalex:W4407173730" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1819662813", + "title": "Big Data's Disparate Impact", + "year": 2016, + "url": "https://doi.org/10.2139/ssrn.2477899", + "doi": "https://doi.org/10.2139/ssrn.2477899", + "abstract": "", + "citation_count": 2147, + "topics": [ + "Ethics and Social Impacts of AI", + "Big Data Technologies and Applications", + "Blockchain Technology Applications and Security" + ], + "discovered_via": [ + "neighbor:openalex:W4318407310", + "neighbor:openalex:W4366989571", + "neighbor:openalex:W4381848566", + "neighbor:openalex:W4400303336" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2004462823", + "title": "Writing narrative literature reviews for peer-reviewed journals: secrets of the trade", + "year": 2006, + "url": "https://doi.org/10.1016/s0899-3467(07)60142-6", + "doi": "https://doi.org/10.1016/s0899-3467(07)60142-6", + "abstract": "", + "citation_count": 2028, + "topics": [ + "Publishing and Scholarly Communication", + "Academic Writing and Publishing", + "Meta-analysis and systematic reviews" + ], + "discovered_via": [ + "neighbor:openalex:W4400303336" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W219375404", + "title": "Speculative everything: design, fiction, and social dreaming", + "year": 2014, + "url": "https://doi.org/10.5860/choice.51-5390", + "doi": "https://doi.org/10.5860/choice.51-5390", + "abstract": "Today designers often focus on making technology easy to use, sexy, and consumable. In Speculative Everything, Anthony Dunne and Fiona Raby propose a kind of design that is used as a tool to create not only things but ideas. For them, design is a means of speculating about how things could be -- to imagine possible futures. This is not the usual sort of predicting or forecasting, spotting trends and extrapolating; these kinds of predictions have been proven wrong, again and again. Instead, Dunne and Raby pose what questions that are intended to open debate and discussion about the kind of future people want (and do not want). Speculative Everything offers a tour through an emerging cultural landscape of design ideas, ideals, and approaches. Dunne and Raby cite examples from their own design and teaching and from other projects from fine art, design, architecture, cinema, and photography. They also draw on futurology, political theory, the philosophy of technology, and literary fiction. They show us, for example, ideas for a solar kitchen restaurant; a flypaper robotic clock; a menstruation machine; a cloud-seeding truck; a phantom-limb sensation recorder; and devices for food foraging that use the tools of synthetic biology. Dunne and Raby contend that if we speculate more -- about everything -- reality will become more malleable. The ideas freed by speculative design increase the odds of achieving desirable futures.", + "citation_count": 1983, + "topics": [ + "Global History, Politics, and Ideology", + "Contemporary Literature and Criticism" + ], + "discovered_via": [ + "neighbor:openalex:W4392182430" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2573660794", + "title": "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy", + "year": 2016, + "url": "https://bvbr.bib-bvb.de:443/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028866887&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA", + "doi": null, + "abstract": "A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life and threaten to rip apart our social fabricWe live in the age of the algorithm. Increasingly, the decisions that affect our liveswhere we go to school, whether we get a car loan, how much we pay for health insuranceare being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated. But as Cathy ONeil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when theyre wrong. Most troubling, they reinforce discrimination: If a poor student cant get a loan because a lending model deems him too risky (by virtue of his zip code), hes then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a toxic cocktail for democracy. Welcome to the dark side of Big Data. Tracing the arc of a persons life, ONeil exposes the black box models that shape our future, both as individuals and as a society. These weapons of math destruction score teachers and students, sort rsums, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health. ONeil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, its up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.", + "citation_count": 1947, + "topics": [ + "Socio-political and Technological Issues" + ], + "discovered_via": [ + "neighbor:openalex:W2992233710" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2040484355", + "title": "Digital divide research, achievements and shortcomings", + "year": 2006, + "url": "https://doi.org/10.1016/j.poetic.2006.05.004", + "doi": "https://doi.org/10.1016/j.poetic.2006.05.004", + "abstract": "", + "citation_count": 1927, + "topics": [ + "ICT Impact and Policies", + "Social Media and Politics", + "Gender and Technology in Education" + ], + "discovered_via": [ + "neighbor:openalex:W4394591980" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2795274753", + "title": "The System Usability Scale: Past, Present, and Future", + "year": 2018, + "url": "https://doi.org/10.1080/10447318.2018.1455307", + "doi": "https://doi.org/10.1080/10447318.2018.1455307", + "abstract": "The System Usability Scale (SUS) is the most widely used standardized questionnaire for the assessment of perceived usability. This review of the SUS covers its early history from inception in the 1980s through recent research and its future prospects. From relatively inauspicious beginnings, when its originator described it as a \u201cquick and dirty usability scale,\u201d it has proven to be quick but not \u201cdirty.\u201d It is likely that the SUS will continue to be a popular measurement of perceived usability for the foreseeable future. When researchers and practitioners need a measure of perceived usability, they should strongly consider using the SUS.", + "citation_count": 1873, + "topics": [ + "Technology Adoption and User Behaviour", + "Usability and User Interface Design", + "Software Engineering Techniques and Practices" + ], + "discovered_via": [ + "neighbor:openalex:W4404254054" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2117735259", + "title": "Computing Inequality: Have Computers Changed the Labor Market?", + "year": 1998, + "url": "https://doi.org/10.1162/003355398555874", + "doi": "https://doi.org/10.1162/003355398555874", + "abstract": "This paper examines the effect of skill-biased technological change as measured by computerization on the recent widening of U. S. educational wage differentials. An analysis of aggregate changes in the relative supplies and wages of workers by education from 1940 to 1996 indicates strong and persistent growth in relative demand favoring college graduates. Rapid skill upgrading within detailed industries accounts for most of the growth in the relative demand for college workers, particularly since 1970. Analyses of four data sets indicate that the rate of skill upgrading has been greater in more computer-intensive industries.", + "citation_count": 1822, + "topics": [ + "Labor market dynamics and wage inequality", + "ICT Impact and Policies", + "Political Economy and Marxism" + ], + "discovered_via": [ + "neighbor:openalex:W4399518864" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2014352947", + "title": "Certifying and Removing Disparate Impact", + "year": 2015, + "url": "https://doi.org/10.1145/2783258.2783311", + "doi": "https://doi.org/10.1145/2783258.2783311", + "abstract": "What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, gender) and an explicit description of the process.", + "citation_count": 1787, + "topics": [ + "Ethics and Social Impacts of AI", + "Law, AI, and Intellectual Property", + "Intellectual Property and Patents" + ], + "discovered_via": [ + "neighbor:openalex:W4387789492" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2106759695", + "title": "Technical Change, Inequality, and the Labor Market", + "year": 2002, + "url": "https://doi.org/10.1257/jel.40.1.7", + "doi": "https://doi.org/10.1257/jel.40.1.7", + "abstract": "This essay discusses the effect of technical change on wage inequality. I argue that the behavior of wages and returns to schooling indicates that technical change has been skill-biased during the past sixty years. Furthermore, the recent increase in inequality is most likely due to an acceleration in skill bias. In contrast to twentieth century developments, most technical change during the nineteenth century appears to be skill-replacing. I suggest that this is because the increased supply of unskilled workers in the English cities made the introduction of these technologies profitable. On the other hand, the twentieth-century has been characterized by skill-biased technical change because the rapid increase in the supply of skilled workers has induced the development of skill-complementary technologies. The recent acceleration in skill bias is in turn likely to have been a response to the acceleration in the supply of skills during the past several decades.", + "citation_count": 1731, + "topics": [ + "Economic Growth and Productivity", + "Economic Theory and Policy", + "Labor market dynamics and wage inequality" + ], + "discovered_via": [ + "neighbor:openalex:W4399518864" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4386142022", + "title": "Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence", + "year": 2023, + "url": "https://doi.org/10.1007/s12559-023-10179-8", + "doi": "https://doi.org/10.1007/s12559-023-10179-8", + "abstract": "Abstract Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottlenecks to adopt such models in mission-critical application domains, such as banking, e-commerce, healthcare, and public services and safety, is the difficulty in interpreting them. Due to the rapid proleferation of these AI models, explaining their learning and decision making process are getting harder which require transparency and easy predictability. Aiming to collate the current state-of-the-art in interpreting the black-box models, this study provides a comprehensive analysis of the explainable AI (XAI) models. To reduce false negative and false positive outcomes of these back-box models, finding flaws in them is still difficult and inefficient. In this paper, the development of XAI is reviewed meticulously through careful selection and analysis of the current state-of-the-art of XAI research. It also provides a comprehensive and in-depth evaluation of the XAI frameworks and their efficacy to serve as a starting point of XAI for applied and theoretical researchers. Towards the end, it highlights emerging and critical issues pertaining to XAI research to showcase major, model-specific trends for better explanation, enhanced transparency, and improved prediction accuracy.", + "citation_count": 1729, + "topics": [ + "Explainable Artificial Intelligence (XAI)", + "Imbalanced Data Classification Techniques", + "Machine Learning in Healthcare" + ], + "discovered_via": [ + "query:AI recruitment system human in the loop recruiter decision support workflow architecture monitoring" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1843425376", + "title": "Fundamentals of Business Process Management", + "year": 2018, + "url": "https://doi.org/10.1007/978-3-662-56509-4", + "doi": "https://doi.org/10.1007/978-3-662-56509-4", + "abstract": "", + "citation_count": 1704, + "topics": [ + "Business Process Modeling and Analysis" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2578240541", + "title": "Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks", + "year": 2017, + "url": "https://doi.org/10.1021/acscentsci.7b00512", + "doi": "https://doi.org/10.1021/acscentsci.7b00512", + "abstract": "drug design cycle to generate large sets of novel molecules for drug discovery.", + "citation_count": 1672, + "topics": [ + "Computational Drug Discovery Methods", + "Machine Learning in Materials Science", + "Chemical Synthesis and Analysis" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1990656690", + "title": "Social Yet Creative: The Role Of Social Relationships In Facilitating Individual Creativity", + "year": 2006, + "url": "https://doi.org/10.5465/amj.2006.20785503", + "doi": "https://doi.org/10.5465/amj.2006.20785503", + "abstract": "Integrating creativity and social network theories, I explore the direct and interactive effects of relationship strength, network position, and external ties on individual creative contributions. Results from a study of research scientists suggest that weaker ties are generally beneficial for creativity, whereas stronger ties have neutral effects. I also found that centrality is more positively associated with creativity when individuals have few ties outside of their organization and that the combination of centrality and many outside ties is not optimal. I discuss the implications of these findings for creativity and social network research.", + "citation_count": 1437, + "topics": [ + "Creativity in Education and Neuroscience", + "Team Dynamics and Performance", + "Business Strategy and Innovation" + ], + "discovered_via": [ + "neighbor:openalex:W4401533174" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1994476912", + "title": "The relative importance of task, citizenship, and counterproductive performance to global ratings of job performance: A policy-capturing approach.", + "year": 2002, + "url": "https://doi.org/10.1037/0021-9010.87.1.66", + "doi": "https://doi.org/10.1037/0021-9010.87.1.66", + "abstract": "A review of research on job performance suggests 3 broad components: task, citizenship, and counterproductive performance. This study examined the relative importance of each component to ratings of overall performance by using an experimental policy-capturing design. Managers in 5 jobs read hypothetical profiles describing employees' task, citizenship, and counterproductive performance and provided global ratings of performance. Within-subjects regression analyses indicated that the weights given to the 3 performance components varied across raters. Hierarchical cluster analyses indicated that raters' policies could be grouped into 3 homogeneous clusters: (a) task performance weighted highest, (b) counterproductive performance weighted highest, and (c) equal and large weights given to task and counterproductive performance. Hierarchical linear modeling indicated that demographic variables were not related to raters' weights.", + "citation_count": 1426, + "topics": [ + "Job Satisfaction and Organizational Behavior", + "Emotional Intelligence and Performance", + "Gender Diversity and Inequality" + ], + "discovered_via": [ + "neighbor:openalex:W4385952870" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W430254451", + "title": "Advances in Organizational Justice", + "year": 2001, + "url": "http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009416642&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA", + "doi": null, + "abstract": "Preface List of figures and tables 1. Fairness theory: justice as accountability Robert Folger and Russell Cropanzano 2. Fairness heuristic theory: justice judgments as pivotal cognitions in organizational relations E. Allan Lind 3. Interactional (in)justice: the sacred and the profane Robert J. Bies 4. Procedurl and distributive justice are more similar than you think: a monistic perspective and a research agenda Russell Cropanzano and Maureen L. Ambrose 5. Anticipatory injustice: the consequences of expecting injustice in the workplace Debra L. Shapiro and Bradley L. Kirkman 6. When do elements of procedural fairness make a difference? a classification to moderating differences Joel Brockner, Grant Ackerman and Gregory Fairchild 7. Ethnic diversity and the viability of organizations: the role of procedural justice in bridging differences Yuen J. Huo and Tom R. Tyler 8. The seven loose can(n)ons of the organizational justice Jerald Greenberg Index.", + "citation_count": 1415, + "topics": [ + "Gender Diversity and Inequality", + "Ethics in Business and Education", + "Job Satisfaction and Organizational Behavior" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2462906003", + "title": "Agnostic Learning with Unknown Utilities", + "year": 2016, + "url": "https://doi.org/10.4230/lipics.concur.2020.3", + "doi": "https://doi.org/10.4230/lipics.concur.2020.3", + "abstract": "Agentic AI systems mark a shift from passive, prompt-driven models to autonomous actors that perceive, plan, and execute actions within enterprise infrastructures. This autonomy introduces risks that exceed conventional bias and safety concerns: agents may manipulate reward structures, obscure trade-offs, and \u2013 by automating routine and peripheral tasks \u2013 erode tacit knowledge and hinder the development of human expertise. Drawing on Critical Theory and labor sociology, this article conceptualizes two structural pathologies of agency: the HAL-9000 problem of unchecked instrumental reason and the Benevolent Mother problem of competence-undermining care. It argues that existing governance frameworks regulate around the system while agentic AI operates within it, producing an autonomy-oversight mismatch. To address this, the article proposes a socio-technical constitutional framework of twelve lexically ordered directives embedded directly into the agent\u2019s decision logic. This framework aims to preserve human autonomy, sustain capability formation, and maintain organizational integrity beyond traditional compliance regimes. Building on a prior conceptual essay that introduced the idea of an \u201cAI constitution\u201d for enterprises using the HAL 9000 metaphor as a narrative device (W\u00fcrdemann, 2025), this article provides a more systematic theoretical framing, formalizes the notion of a constitutional layer for agentic AI, and develops a structured set of directives for enterprise practice and future research.", + "citation_count": 1405, + "topics": [ + "Adversarial Robustness in Machine Learning", + "Ethics and Social Impacts of AI", + "Explainable Artificial Intelligence (XAI)" + ], + "discovered_via": [ + "neighbor:openalex:W4381848566" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2012820235", + "title": "The benefits of publishing systematic quantitative literature reviews for PhD candidates and other early-career researchers", + "year": 2013, + "url": "https://doi.org/10.1080/07294360.2013.841651", + "doi": "https://doi.org/10.1080/07294360.2013.841651", + "abstract": "Universities increasingly expect students to publish during a PhD candidature because it benefits the candidate, supervisor, institution, and wider community. Here, we describe a method successfully used by early-career researchers including PhD candidates to undertake and publish literature reviews \u2013 a challenge for researchers new to a field. Our method allows researchers new to a field to systematically analyse existing academic literature to produce a structured quantitative summary of the field. This method is a more straightforward and systematic approach than the traditional \u2018narrative method\u2019 common to many student theses. When published, this type of review can also complement existing narrative reviews produced by experts in a field by quantitatively assessing the literature, including identifying research gaps. The method can also be used as the initial step for further analysis, including identifying suitable datasets for meta-analysis. Students report that the method is enabling and rewarding.", + "citation_count": 1304, + "topics": [ + "Meta-analysis and systematic reviews", + "Doctoral Education Challenges and Solutions", + "Academic Writing and Publishing" + ], + "discovered_via": [ + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2116984840", + "title": "Data preprocessing techniques for classification without discrimination", + "year": 2011, + "url": "https://doi.org/10.1007/s10115-011-0463-8", + "doi": "https://doi.org/10.1007/s10115-011-0463-8", + "abstract": "Recently, the following Discrimination-Aware Classification Problem was introduced: Suppose we are given training data that exhibit unlawful discrimination; e.g., toward sensitive attributes such as gender or ethnicity. The task is to learn a classifier that optimizes accuracy, but does not have this discrimination in its predictions on test data. This problem is relevant in many settings, such as when the data are generated by a biased decision process or when the sensitive attribute serves as a proxy for unobserved features. In this paper, we concentrate on the case with only one binary sensitive attribute and a two-class classification problem. We first study the theoretically optimal trade-off between accuracy and non-discrimination for pure classifiers. Then, we look at algorithmic solutions that preprocess the data to remove discrimination before a classifier is learned. We survey and extend our existing data preprocessing techniques, being suppression of the sensitive attribute, massaging the dataset by changing class labels, and reweighing or resampling the data to remove discrimination without relabeling instances. These preprocessing techniques have been implemented in a modified version of Weka and we present the results of experiments on real-life data.", + "citation_count": 1251, + "topics": [ + "Imbalanced Data Classification Techniques", + "Machine Learning and Data Classification", + "Machine Learning and Algorithms" + ], + "discovered_via": [ + "neighbor:openalex:W4387789492" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2124355949", + "title": "The Utility of Simulation in Medical Education: What Is the Evidence?", + "year": 2009, + "url": "https://doi.org/10.1002/msj.20127", + "doi": "https://doi.org/10.1002/msj.20127", + "abstract": "Medical schools and residencies are currently facing a shift in their teaching paradigm. The increasing amount of medical information and research makes it difficult for medical education to stay current in its curriculum. As patients become increasingly concerned that students and residents are \"practicing\" on them, clinical medicine is becoming focused more on patient safety and quality than on bedside teaching and education. Educators have faced these challenges by restructuring curricula, developing small-group sessions, and increasing self-directed learning and independent research. Nevertheless, a disconnect still exists between the classroom and the clinical environment. Many students feel that they are inadequately trained in history taking, physical examination, diagnosis, and management. Medical simulation has been proposed as a technique to bridge this educational gap. This article reviews the evidence for the utility of simulation in medical education. We conducted a MEDLINE search of original articles and review articles related to simulation in education with key words such as simulation, mannequin simulator, partial task simulator, graduate medical education, undergraduate medical education, and continuing medical education. Articles, related to undergraduate medical education, graduate medical education, and continuing medical education were used in the review. One hundred thirteen articles were included in this review. Simulation-based training was demonstrated to lead to clinical improvement in 2 areas of simulation research. Residents trained on laparoscopic surgery simulators showed improvement in procedural performance in the operating room. The other study showed that residents trained on simulators were more likely to adhere to the advanced cardiac life support protocol than those who received standard training for cardiac arrest patients. In other areas of medical training, simulation has been demonstrated to lead to improvements in medical knowledge, comfort in procedures, and improvements in performance during retesting in simulated scenarios. Simulation has also been shown to be a reliable tool for assessing learners and for teaching topics such as teamwork and communication. Only a few studies have shown direct improvements in clinical outcomes from the use of simulation for training. Multiple studies have demonstrated the effectiveness of simulation in the teaching of basic science and clinical knowledge, procedural skills, teamwork, and communication as well as assessment at the undergraduate and graduate medical education levels. As simulation becomes increasingly prevalent in medical school and resident education, more studies are needed to see if simulation training improves patient outcomes.", + "citation_count": 1201, + "topics": [ + "Simulation-Based Education in Healthcare", + "Surgical Simulation and Training", + "Innovations in Medical Education" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1905153633", + "title": "Age and gender classification using convolutional neural networks", + "year": 2015, + "url": "https://doi.org/10.1109/cvprw.2015.7301352", + "doi": "https://doi.org/10.1109/cvprw.2015.7301352", + "abstract": "Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. In this paper we show that by learning representations through the use of deep-convolutional neural networks (CNN), a significant increase in performance can be obtained on these tasks. To this end, we propose a simple convolutional net architecture that can be used even when the amount of learning data is limited. We evaluate our method on the recent Adience benchmark for age and gender estimation and show it to dramatically outperform current state-of-the-art methods.", + "citation_count": 1153, + "topics": [ + "Face recognition and analysis", + "Face and Expression Recognition", + "Human Pose and Action Recognition" + ], + "discovered_via": [ + "neighbor:openalex:W4392172846" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1989245429", + "title": "Harrison's Principles of Internal Medicine", + "year": 1984, + "url": "https://doi.org/10.1001/jama.1984.03340300091039", + "doi": "https://doi.org/10.1001/jama.1984.03340300091039", + "abstract": "The first modern textbook of internal medicine was Osler'sThe Principles and Practice of Medicine, published in 1892. This was made possible by the development of bacteriology in the last third of the 19th century, and the consequent assignment of specific causes to the illnesses most patients were initially seen with in those days. For the first time it was practical to have a nosography based on etiology. Osler's breadth of knowledge, clinical orientation, and mastery of English prose made the book a tremendous success. New editions appeared every two or three years. In 1927, an entirely new textbook of medicine came out, edited by Dr Russell Cecil, but written by 130 contributors. It soon replacedOsler(by then edited by McCrae) as the standard American medical text. Cecil retained Osler's format, with discrete descriptions of \"disease entities\" grouped by etiology or by systems affected. In 1950, another textbook was", + "citation_count": 1138, + "topics": [ + "Empathy and Medical Education", + "History of Medical Practice", + "Clinical Reasoning and Diagnostic Skills" + ], + "discovered_via": [ + "neighbor:openalex:W4392172846" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2006447892", + "title": "Bias in computer systems", + "year": 1996, + "url": "https://doi.org/10.1145/230538.230561", + "doi": "https://doi.org/10.1145/230538.230561", + "abstract": "From an analysis of actual cases, three categories of bias in computer systems have been developed: preexisting, technical, and emergent. Preexisting bias has its roots in social institutions, practices, and attitudes. Technical bias arises from technical constraints of considerations. Emergent bias arises in a context of use. Although others have pointed to bias inparticular computer systems and have noted the general problem, we know of no comparable work that examines this phenomenon comprehensively and which offers a framework for understanding and remedying it. We conclude by suggesting that freedom from bias should by counted amoung the select set of criteria\u2014including reliability, accuracy, and efficiency\u2014according to which the quality of systems in use in society should be judged.", + "citation_count": 1103, + "topics": [ + "Auction Theory and Applications", + "Game Theory and Voting Systems", + "Game Theory and Applications" + ], + "discovered_via": [ + "neighbor:openalex:W4392172846" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W598355015", + "title": "Evaluating the Quality of Learning: The Solo Taxonomy : Structure of the Observed Learning Outcome", + "year": 1977, + "url": "https://bvbr.bib-bvb.de:443/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=000087129&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA", + "doi": null, + "abstract": "", + "citation_count": 1090, + "topics": [ + "Evaluation and Performance Assessment" + ], + "discovered_via": [ + "neighbor:openalex:W4390315357" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2049757356", + "title": "Recent advances and trends in predictive manufacturing systems in big data environment", + "year": 2013, + "url": "https://doi.org/10.1016/j.mfglet.2013.09.005", + "doi": "https://doi.org/10.1016/j.mfglet.2013.09.005", + "abstract": "", + "citation_count": 1057, + "topics": [ + "Industrial Vision Systems and Defect Detection", + "Digital Transformation in Industry" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3123374861", + "title": "Big Data\ufffds Disparate Impact", + "year": 2016, + "url": "https://doi.org/10.15779/z38bg31", + "doi": "https://doi.org/10.15779/z38bg31", + "abstract": "Advocates of algorithmic techniques like data mining argue that these techniques eliminate human biases from the decision-making process. But an algorithm is only as good as the data it works with. Data is frequently imperfect in ways that allow these algorithms to inherit the prejudices of prior decision makers. In other cases, data may simply reflect the widespread biases that persist in society at large. In still others, data mining can discover surprisingly useful regularities that are really just preexisting patterns of exclusion and inequality. Unthinking reliance on data mining can deny historically disadvantaged and vulnerable groups full participation in society. Worse still, because the resulting discrimination is almost always an unintentional emergent property of the algorithm\u2019s use rather than a conscious choice by its programmers, it can be unusually hard to identify the source of the problem or to explain it to a court.This Essay examines these concerns through the lens of American antidiscrimination law \u2014 more particularly, through Title VII\u2019s prohibition of discrimination in employment. In the absence of a demonstrable intent to discriminate, the best doctrinal hope for data mining\u2019s victims would seem to lie in disparate impact doctrine. Case law and the Equal Employment Opportunity Commission\u2019s Uniform Guidelines, though, hold that a practice can be justified as a business necessity when its outcomes are predictive of future employment outcomes, and data mining is specifically designed to find such statistical correlations. Unless there is a reasonably practical way to demonstrate that these discoveries are spurious, Title VII would appear to bless its use, even though the correlations it discovers will often reflect historic patterns of prejudice, others\u2019 discrimination against members of protected groups, or flaws in the underlying dataAddressing the sources of this unintentional discrimination and remedying the corresponding deficiencies in the law will be difficult technically, difficult legally, and difficult politically. There are a number of practical limits to what can be accomplished computationally. For example, when discrimination occurs because the data being mined is itself a result of past intentional discrimination, there is frequently no obvious method to adjust historical data to rid it of this taint. Corrective measures that alter the results of the data mining after it is complete would tread on legally and politically disputed terrain. These challenges for reform throw into stark relief the tension between the two major theories underlying antidiscrimination law: anticlassification and antisubordination. Finding a solution to big data\u2019s disparate impact will require more than best efforts to stamp out prejudice and bias; it will require a wholesale reexamination of the meanings of \u201cdiscrimination\u201d and \u201cfairness.\u201d", + "citation_count": 1056, + "topics": [ + "Law, AI, and Intellectual Property", + "Law, Economics, and Judicial Systems", + "Legal and Constitutional Studies" + ], + "discovered_via": [ + "neighbor:openalex:W2992233710" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2010992114", + "title": "The Social Shaping of Technology", + "year": 1987, + "url": "https://doi.org/10.2307/3105489", + "doi": "https://doi.org/10.2307/3105489", + "abstract": "Technological change is often seen as something that follows its own logic - something we may welcome, or about which we may protest, but which we are unable to alter fundamentally. This reader challenges that assumption and its distinguished contributors demonstrate that technology is affected at a fundamental level by the social context in which it develops. General arguments are introduced about the relation of technology to society and different types of technology are examined: the technology of production; domestic and reproductive technology; and military technology. The book draws on authors from Karl Marx to Cynthia Cockburn to show that production technology is shaped by social relations in the workplace. It moves on to the technologies of the household and biological reproduction, which are topics that male-dominated social science has tended to ignore or trivialise - though these are actually of crucial significance where powerful shaping factors are at work, normally unnoticed. The final section asks what shapes the most frightening technology of all - the technology of weaponry, especially nuclear weapons. The editors argue that social scientists have devoted disproportionate attention to the effects of technology on society, and tended to ignore the more fundamental question of what shapes technology in the first place. They have drawn both on established work in the history and sociology of technology and on newer feminist perspectives to show just how important and fruitful it is to try to answer that deeper question. The first edition of this reader, published in 1985, had a considerable influence on thinking about the relationship between technology and society. This second edition has been thoroughly revised and expanded to take into account new research and the emergence of new theoretical perspectives.", + "citation_count": 1045, + "topics": [ + "Anthropology: Ethics, History, Culture" + ], + "discovered_via": [ + "neighbor:openalex:W4366989571" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1502678715", + "title": "Social Desirability Bias", + "year": 2010, + "url": "https://doi.org/10.1002/9781444316568.wiem02057", + "doi": "https://doi.org/10.1002/9781444316568.wiem02057", + "abstract": "Abstract Social desirability bias refers to the tendency of research subjects to give socially desirable responses instead of choosing responses that are reflective of their true feelings. The bias in responses due to this personality trait becomes a major issue when the scope of the study involves socially sensitive issues such as politics, religion, and environment, or personal issues such as drug use, cheating, and smoking. Whenever possible, it is desirable to measure the extent of the bias present in responses to a survey by incorporating a socially desirable scale in the survey. A number of methods to address this issue are suggested in the literature. Use of a well\u2010trained interviewer or collection of data through methods that do not require presence involvement of an interviewer can help avoid this bias to some extent. Properly identified options to questions vulnerable to social desirability effect is another means of tackling this issue.", + "citation_count": 1034, + "topics": [ + "Survey Methodology and Nonresponse", + "Social and Intergroup Psychology", + "Survey Sampling and Estimation Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4409825334" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1716366172", + "title": "Psychological Testing and Assessment: An Introduction to Tests and Measurement", + "year": 1992, + "url": "http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018600688&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA", + "doi": null, + "abstract": "CHAPTER 1 Psychological Testing and Assessment CHAPTER 2 Historical, Cultural, and Legal/Ethical Considerations CHAPTER 3 A Statistics Refresher CHAPTER 4 Of Tests and Testing CHAPTER 5 Reliability CHAPTER 6 Validity CHAPTER 7 Utility CHAPTER 8 Test Development CHAPTER 9 Intelligence and Its Measurement CHAPTER 10 Tests of Intelligence CHAPTER 11 Assessment for Education CHAPTER 12 Personality Assessment: An Overview CHAPTER 13 Personality Assessment Methods CHAPTER 14 Clinical and Counseling Assessment CHAPTER 15 Neuropsychological Assessment CHAPTER 16 Assessment, Careers, and Business References Credits Name Index Glossary/Index", + "citation_count": 999, + "topics": [ + "Counseling Practices and Supervision", + "Psychological Testing and Assessment" + ], + "discovered_via": [ + "neighbor:openalex:W4409825334" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2057364222", + "title": "Privacy as a Concept and a Social Issue: A Multidimensional Developmental Theory", + "year": 1977, + "url": "https://doi.org/10.1111/j.1540-4560.1977.tb01880.x", + "doi": "https://doi.org/10.1111/j.1540-4560.1977.tb01880.x", + "abstract": "If we are to understand privacy as a future as well as a contemporary social issue, we must understand privacy as a concept. Individuals' concepts of privacy are tied to concrete situations in everyday life. These situations are described in terms of three dimensions: self\u2010ego, environmental, and interpersonal. In combination with the dynamic of time, both developmental and sociohistorical, this situational analysis helps us to understand individual perceptions of privacy and privacy invasion, to predict potential privacy or invasion experiences, and to see the potential effects of the absence of certain privacy\u2010related experiences.", + "citation_count": 977, + "topics": [ + "Attachment and Relationship Dynamics", + "Cultural Differences and Values", + "Community Health and Development" + ], + "discovered_via": [ + "neighbor:openalex:W4399518864" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1979378757", + "title": "Gender Differences in Personality across the Ten Aspects of the Big Five", + "year": 2011, + "url": "https://doi.org/10.3389/fpsyg.2011.00178", + "doi": "https://doi.org/10.3389/fpsyg.2011.00178", + "abstract": "This paper investigates gender differences in personality traits, both at the level of the Big Five and at the sublevel of two aspects within each Big Five domain. Replicating previous findings, women reported higher Big Five Extraversion, Agreeableness, and Neuroticism scores than men. However, more extensive gender differences were found at the level of the aspects, with significant gender differences appearing in both aspects of every Big Five trait. For Extraversion, Openness, and Conscientiousness, the gender differences were found to diverge at the aspect level, rendering them either small or undetectable at the Big Five level. These findings clarify the nature of gender differences in personality and highlight the utility of measuring personality at the aspect level.", + "citation_count": 931, + "topics": [ + "Personality Traits and Psychology", + "Evolutionary Psychology and Human Behavior", + "Personality Disorders and Psychopathology" + ], + "discovered_via": [ + "neighbor:openalex:W4385952870" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2002980572", + "title": "A Systematic Literature Review of Servant Leadership Theory in Organizational Contexts", + "year": 2012, + "url": "https://doi.org/10.1007/s10551-012-1322-6", + "doi": "https://doi.org/10.1007/s10551-012-1322-6", + "abstract": "", + "citation_count": 930, + "topics": [ + "Job Satisfaction and Organizational Behavior", + "Organizational Leadership and Management Strategies", + "Workplace Spirituality and Leadership" + ], + "discovered_via": [ + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1730560180", + "title": "Fairness heuristic theory: Justice judgments as pivotal cognitions in organizational relations.", + "year": 2001, + "url": "https://psycnet.apa.org/record/2001-06428-002", + "doi": null, + "abstract": "", + "citation_count": 885, + "topics": [ + "Experimental Behavioral Economics Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2095026705", + "title": "Service as business logic: implications for value creation and marketing", + "year": 2011, + "url": "https://doi.org/10.1108/09564231111106893", + "doi": "https://doi.org/10.1108/09564231111106893", + "abstract": "Purpose The purpose of this article is to analyze the scope, content and nature of value co\u2010creation in a service logic\u2010based view of value creation, addressing the customer's perspective in a supplier\u2010customer relationship. The nature of the activities and the roles of the supplier and the customer in value creation and co\u2010creation are analyzed. Furthermore, the purpose is to discuss what implications for marketing can be derived from this analysis. Design/methodology/approach The article analyzes the marketing implications that follow from the pivotal role of interactions in service provision. The article, thus, builds on a long history in service marketing research pointing at the impact on the content and scope of marketing of customer\u2010supplier interactions. Findings In this article, it is concluded that creating customer value is a multilaned process consisting of two conceptually distinct subprocesses. These are the supplier's process of providing resources for customer's use and the customer's process of turning service into value. The article results in five service logic theses which provide an understanding of the process of value creation and its implications for marketing. The theses offer a terminology that helps researchers and practitioners to understand the various roles of suppliers and customers in value creation and to analyze opportunities for co\u2010creation of value. Originality/value The findings of this article challenge some of the salient propositions of the emerging service\u2010dominant logic, i.e. customers as co\u2010creators of value, and firms can only make value propositions. The role of marketing is reframed beyond its conventional borders.", + "citation_count": 882, + "topics": [ + "Service and Product Innovation", + "Customer Service Quality and Loyalty", + "Consumer Behavior in Brand Consumption and Identification" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "Concerns service-marketing value creation rather than AI or hiring processes.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W1965443741", + "title": "A common language effect size statistic.", + "year": 1992, + "url": "https://doi.org/10.1037/0033-2909.111.2.361", + "doi": "https://doi.org/10.1037/0033-2909.111.2.361", + "abstract": "Some of the shortcomings in interpretability and generalizability of the effect size statistics currently available to researchers can be overcome by a statistic that expresses how often a score sampled from one distribution will be greater than a score sampled from another distribution. The statistic, the common language effect size indicator, is easily calculated from sample means and variances (or from proportions in the case of nominal-level data). It can be used for expressing the effect observed in both independent and related sample designs and in both 2-group and n-group designs", + "citation_count": 880, + "topics": [ + "Advanced Statistical Modeling Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4392182430" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2300445845", + "title": "Interactive machine learning for health informatics: when do we need the human-in-the-loop?", + "year": 2016, + "url": "https://doi.org/10.1007/s40708-016-0042-6", + "doi": "https://doi.org/10.1007/s40708-016-0042-6", + "abstract": "Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as \"algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.\" This \"human-in-the-loop\" can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.", + "citation_count": 875, + "topics": [ + "Machine Learning and Algorithms", + "Data Stream Mining Techniques", + "Mobile Crowdsensing and Crowdsourcing" + ], + "discovered_via": [ + "neighbor:openalex:W4381848566" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2085988980", + "title": "The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients", + "year": 2008, + "url": "https://doi.org/10.1016/j.eswa.2007.12.020", + "doi": "https://doi.org/10.1016/j.eswa.2007.12.020", + "abstract": "", + "citation_count": 863, + "topics": [ + "Financial Distress and Bankruptcy Prediction", + "Imbalanced Data Classification Techniques", + "Data Mining Algorithms and Applications" + ], + "discovered_via": [ + "neighbor:openalex:W4316038168" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2917193084", + "title": "From DFT to machine learning: recent approaches to materials science\u2013a review", + "year": 2019, + "url": "https://doi.org/10.1088/2515-7639/ab084b", + "doi": "https://doi.org/10.1088/2515-7639/ab084b", + "abstract": "Abstract Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field.", + "citation_count": 840, + "topics": [ + "Machine Learning in Materials Science", + "X-ray Diffraction in Crystallography", + "Electronic and Structural Properties of Oxides" + ], + "discovered_via": [ + "neighbor:openalex:W4381684347" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2120946119", + "title": "A Consumer Perspective on Value Creation", + "year": 2007, + "url": "https://doi.org/10.5465/amr.2007.23464055", + "doi": "https://doi.org/10.5465/amr.2007.23464055", + "abstract": "I show how company strategies that focus on improving consumer benefits can create value by increasing consumer payments to an entire value system. This \u201cconsumer benefit experienced\u201d viewpoint on value creation complements the value capture orientations of the firm positioning, transaction cost, and resource-based approaches. It helps to clarify often-blurred distinctions between value creation and value capture and offers an additional tool for addressing intractable issues in strategic management.", + "citation_count": 816, + "topics": [ + "Innovation and Knowledge Management", + "Business Strategy and Innovation", + "Service and Product Innovation" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1597835403", + "title": "Reason and the rationalization of society", + "year": 1984, + "url": "http://ci.nii.ac.jp/ncid/BA84196146", + "doi": null, + "abstract": "Translator's Introduction. Author's Preface. I. Introduction: Approaches to the Problem of Rationality. 1. - A Preliminary Specification. 2. Some Characteristics of the Mythical and the Modern Ways of Understanding the World. 3. Relations to the World and Aspects of Rationality in Four Sociological Concepts of Action. 4. The Problem of Understanding Meaning in the Social Sciences. II. Max Weber's Theory of Rationalization. 1. Occidental Rationalism. 2. The Disenchantment of Religious-Metaphysical Worldviews and the Emergence of Modern Structures of Consciousness. 3. Modernization as Societal Rationalization: The Role of the Protestant Ethic. 4. The Rationalization of Law: Weber's Diagnosis of the Times. III. Intermediate Reflections: Social Action, Purposive Activity, and Communication. IV. From Lukacs to Adorno: Rationalization as Reification. 1. Max Weber in the Tradition of Western Marxism. 2. The Critique of Instrumental Reason.", + "citation_count": 816, + "topics": [ + "Critical Theory and Philosophy" + ], + "discovered_via": [ + "neighbor:openalex:W4383913712" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2323368791", + "title": "Durably reducing transphobia: A field experiment on door-to-door canvassing", + "year": 2016, + "url": "https://doi.org/10.1126/science.aad9713", + "doi": "https://doi.org/10.1126/science.aad9713", + "abstract": "Existing research depicts intergroup prejudices as deeply ingrained, requiring intense intervention to lastingly reduce. Here, we show that a single approximately 10-minute conversation encouraging actively taking the perspective of others can markedly reduce prejudice for at least 3 months. We illustrate this potential with a door-to-door canvassing intervention in South Florida targeting antitransgender prejudice. Despite declines in homophobia, transphobia remains pervasive. For the intervention, 56 canvassers went door to door encouraging active perspective-taking with 501 voters at voters' doorsteps. A randomized trial found that these conversations substantially reduced transphobia, with decreases greater than Americans' average decrease in homophobia from 1998 to 2012. These effects persisted for 3 months, and both transgender and nontransgender canvassers were effective. The intervention also increased support for a nondiscrimination law, even after exposing voters to counterarguments.", + "citation_count": 777, + "topics": [ + "Consumer Behavior in Brand Consumption and Identification", + "Consumer Market Behavior and Pricing", + "Media, Gender, and Advertising" + ], + "discovered_via": [ + "neighbor:openalex:W4407762039" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2940010972", + "title": "XGBoost Model for Chronic Kidney Disease Diagnosis", + "year": 2019, + "url": "https://doi.org/10.1109/tcbb.2019.2911071", + "doi": "https://doi.org/10.1109/tcbb.2019.2911071", + "abstract": "Chronic Kidney Disease (CKD) is a menace that is affecting 10 percent of the world population and 15 percent of the South African population. The early and cheap diagnosis of this disease with accuracy and reliability will save 20,000 lives in South Africa per year. Scientists are developing smart solutions with Artificial Intelligence (AI). In this paper, several typical and recent AI algorithms are studied in the context of CKD and the extreme gradient boosting (XGBoost) is chosen as our base model for its high performance. Then, the model is optimized and the optimal full model trained on all the features achieves a testing accuracy, sensitivity, and specificity of 1.000, 1.000, and 1.000, respectively. Note that, to cover the widest range of people, the time and monetary costs of CKD diagnosis have to be minimized with fewest patient tests. Thus, the reduced model using fewer features is desirable while it should still maintain high performance. To this end, the set-theory based rule is presented which combines a few feature selection methods with their collective strengths. The reduced model using about a half of the original full features performs better than the models based on individual feature selection methods and achieves accuracy, sensitivity and specificity, of 1.000, 1.000, and 1.000, respectively.", + "citation_count": 776, + "topics": [ + "Artificial Intelligence in Healthcare", + "Machine Learning in Healthcare" + ], + "discovered_via": [ + "neighbor:openalex:W4404254054" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2097246321", + "title": "Three naive Bayes approaches for discrimination-free classification", + "year": 2010, + "url": "https://doi.org/10.1007/s10618-010-0190-x", + "doi": "https://doi.org/10.1007/s10618-010-0190-x", + "abstract": "In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to be independent with respect to a given sensitive attribute. Such independency restrictions occur naturally when the decision process leading to the labels in the data-set was biased; e.g., due to gender or racial discrimination. This setting is motivated by many cases in which there exist laws that disallow a decision that is partly based on discrimination. Naive application of machine learning techniques would result in huge fines for companies. We present three approaches for making the naive Bayes classifier discrimination-free: (i) modifying the probability of the decision being positive, (ii) training one model for every sensitive attribute value and balancing them, and (iii) adding a latent variable to the Bayesian model that represents the unbiased label and optimizing the model parameters for likelihood using expectation maximization. We present experiments for the three approaches on both artificial and real-life data.", + "citation_count": 773, + "topics": [ + "Imbalanced Data Classification Techniques", + "Machine Learning and Data Classification", + "Data Mining Algorithms and Applications" + ], + "discovered_via": [ + "neighbor:openalex:W4387789492" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1949778830", + "title": "Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A", + "year": 2015, + "url": "https://doi.org/10.1109/cvpr.2015.7298803", + "doi": "https://doi.org/10.1109/cvpr.2015.7298803", + "abstract": "Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is the use of a commodity face detector to select face imagery. The implication of this strategy is restricted variations in face pose and other confounding factors. This paper introduces the IARPA Janus Benchmark A (IJB-A), a publicly available media in the wild dataset containing 500 subjects with manually localized face images. Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1:N search) and verification (1:1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations. The dataset has been developed using 1,501,267 million crowd sourced annotations. Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark.", + "citation_count": 761, + "topics": [ + "Face recognition and analysis", + "Face and Expression Recognition", + "Biometric Identification and Security" + ], + "discovered_via": [ + "neighbor:openalex:W4392172846" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1877361684", + "title": "The double\u2010edged sword of recombination in breakthrough innovation", + "year": 2014, + "url": "https://doi.org/10.1002/smj.2294", + "doi": "https://doi.org/10.1002/smj.2294", + "abstract": "We explore the double\u2010edged sword of recombination in generating breakthrough innovation: recombination of distant or diverse knowledge is needed because knowledge in a narrow domain might trigger myopia, but recombination can be counterproductive when local search is needed to identify anomalies. We take into account how creativity shapes both the cognitive novelty of the idea and the subsequent realization of economic value. We develop a text\u2010based measure of novel ideas in patents using topic modeling to identify those patents that originate new topics in a body of knowledge. We find that, counter to theories of recombination, patents that originate new topics are more likely to be associated with local search, while economic value is the product of broader recombinations as well as novelty . Copyright \u00a9 2014 John Wiley & Sons, Ltd.", + "citation_count": 744, + "topics": [ + "Machine Learning in Materials Science", + "scientometrics and bibliometrics research", + "Intellectual Property and Patents" + ], + "discovered_via": [ + "neighbor:openalex:W4401533174" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W282787389", + "title": "Handbook of Test Development", + "year": 2015, + "url": "https://doi.org/10.4324/9780203102961", + "doi": "https://doi.org/10.4324/9780203102961", + "abstract": "pt. I. Foundations -- pt. II. Content -- pt. III. Item development and scoring -- pt. IV. Test design and assembly -- pt. V. Production, preparation, administration, reporting, documentation and evaluation", + "citation_count": 729, + "topics": [ + "Student Assessment and Feedback" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2026019770", + "title": "Discrimination-aware data mining", + "year": 2008, + "url": "https://doi.org/10.1145/1401890.1401959", + "doi": "https://doi.org/10.1145/1401890.1401959", + "abstract": "In the context of civil rights law, discrimination refers to unfair or unequal treatment of people based on membership to a category or a minority, without regard to individual merit. Rules extracted from databases by data mining techniques, such as classification or association rules, when used for decision tasks such as benefit or credit approval, can be discriminatory in the above sense. In this paper, the notion of discriminatory classification rules is introduced and studied. Providing a guarantee of non-discrimination is shown to be a non trivial task. A naive approach, like taking away all discriminatory attributes, is shown to be not enough when other background knowledge is available. Our approach leads to a precise formulation of the redlining problem along with a formal result relating discriminatory rules with apparently safe ones by means of background knowledge. An empirical assessment of the results on the German credit dataset is also provided.", + "citation_count": 681, + "topics": [ + "Imbalanced Data Classification Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4381848566" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2886619852", + "title": "Are women greener? Corporate gender diversity and environmental violations", + "year": 2018, + "url": "https://doi.org/10.1016/j.jcorpfin.2018.08.004", + "doi": "https://doi.org/10.1016/j.jcorpfin.2018.08.004", + "abstract": "", + "citation_count": 670, + "topics": [ + "Corporate Social Responsibility Reporting", + "Environmental Sustainability in Business", + "Gender Diversity and Inequality" + ], + "discovered_via": [ + "neighbor:openalex:W4322742241" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1648303880", + "title": "Automated Experiments on Ad Privacy Settings", + "year": 2015, + "url": "https://doi.org/10.1515/popets-2015-0007", + "doi": "https://doi.org/10.1515/popets-2015-0007", + "abstract": "Abstract To partly address people\u2019s concerns over web tracking, Google has created the Ad Settings webpage to provide information about and some choice over the profiles Google creates on users. We present AdFisher, an automated tool that explores how user behaviors, Google\u2019s ads, and Ad Settings interact. AdFisher can run browser-based experiments and analyze data using machine learning and significance tests. Our tool uses a rigorous experimental design and statistical analysis to ensure the statistical soundness of our results. We use AdFisher to find that the Ad Settings was opaque about some features of a user\u2019s profile, that it does provide some choice on ads, and that these choices can lead to seemingly discriminatory ads. In particular, we found that visiting webpages associated with substance abuse changed the ads shown but not the settings page. We also found that setting the gender to female resulted in getting fewer instances of an ad related to high paying jobs than setting it to male. We cannot determine who caused these findings due to our limited visibility into the ad ecosystem, which includes Google, advertisers, websites, and users. Nevertheless, these results can form the starting point for deeper investigations by either the companies themselves or by regulatory bodies.", + "citation_count": 666, + "topics": [ + "Privacy, Security, and Data Protection", + "Sexuality, Behavior, and Technology", + "Consumer Market Behavior and Pricing" + ], + "discovered_via": [ + "neighbor:openalex:W4318407310", + "neighbor:openalex:W4366989571" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2614794251", + "title": "Two Decades of Recommender Systems at Amazon.com", + "year": 2017, + "url": "https://doi.org/10.1109/mic.2017.72", + "doi": "https://doi.org/10.1109/mic.2017.72", + "abstract": "Amazon is well-known for personalization and recommendations, which help customers discover items they might otherwise not have found. In this update to their original paper, the authors discuss some of the changes as Amazon has grown.", + "citation_count": 652, + "topics": [ + "Recommender Systems and Techniques", + "Image Retrieval and Classification Techniques", + "Data Management and Algorithms" + ], + "discovered_via": [ + "neighbor:openalex:W4316038168" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2263936035", + "title": "Accountability in algorithmic decision making", + "year": 2016, + "url": "https://doi.org/10.1145/2844110", + "doi": "https://doi.org/10.1145/2844110", + "abstract": "A view from computational journalism.", + "citation_count": 642, + "topics": [ + "Ethics and Social Impacts of AI", + "Explainable Artificial Intelligence (XAI)", + "Big Data and Business Intelligence" + ], + "discovered_via": [ + "neighbor:openalex:W4400303336" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2103608739", + "title": "RECONSIDERING THE USE OF PERSONALITY TESTS IN PERSONNEL SELECTION CONTEXTS", + "year": 2007, + "url": "https://doi.org/10.1111/j.1744-6570.2007.00089.x", + "doi": "https://doi.org/10.1111/j.1744-6570.2007.00089.x", + "abstract": "Although long thought to be unrelated to job performance, research in the early 1990s provided evidence that personality can predict job performance. Accompanying this research was a resurgence of interest in the use of personality tests in high\u2010stakes selection environments. Yet there are numerous potential problems associated with the current operational use of personality. As such, 5 former journal editors from Personnel Psychology and the Journal of Applied Psychology (2 primary outlets for such research), who have collectively reviewed over 7,000 manuscripts and who have no vested interest in personality testing, reconsider the research on the use of personality tests in environments where important selection decisions are made. Their comments are based on a panel discussion held at the 2004 SIOP conference. Collectively, they come to several conclusions. First, faking on self\u2010report personality tests cannot be avoided and perhaps is not the issue; the issue is the very low validity of personality tests for predicting job performance. Second, as such, using published self\u2010report personality tests in selection contexts should be reconsidered. Third, personality constructs may have value for employee selection, but future research should focus on finding alternatives to self\u2010report personality measures.", + "citation_count": 640, + "topics": [ + "Personality Traits and Psychology", + "Job Satisfaction and Organizational Behavior", + "Emotional Intelligence and Performance" + ], + "discovered_via": [ + "neighbor:openalex:W4385952870" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1987964272", + "title": "Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent", + "year": 2011, + "url": "https://doi.org/10.1016/j.ecolmodel.2011.04.011", + "doi": "https://doi.org/10.1016/j.ecolmodel.2011.04.011", + "abstract": "", + "citation_count": 584, + "topics": [ + "Species Distribution and Climate Change", + "Wildlife Ecology and Conservation", + "Genetic diversity and population structure" + ], + "discovered_via": [ + "neighbor:openalex:W4392172846" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1997439558", + "title": "A meta-analysis of gender stereotypes and bias in experimental simulations of employment decision making.", + "year": 2014, + "url": "https://doi.org/10.1037/a0036734", + "doi": "https://doi.org/10.1037/a0036734", + "abstract": "Gender bias continues to be a concern in many work settings, leading researchers to identify factors that influence workplace decisions. In this study we examine several of these factors, using an organizing framework of sex distribution within jobs (including male- and female-dominated jobs as well as sex-balanced, or integrated, jobs). We conducted random effects meta-analyses including 136 independent effect sizes from experimental studies (N = 22,348) and examined the effects of decision-maker gender, amount and content of information available to the decision maker, type of evaluation, and motivation to make careful decisions on gender bias in organizational decisions. We also examined study characteristics such as type of participant, publication year, and study design. Our findings revealed that men were preferred for male-dominated jobs (i.e., gender-role congruity bias), whereas no strong preference for either gender was found for female-dominated or integrated jobs. Second, male raters exhibited greater gender-role congruity bias than did female raters for male-dominated jobs. Third, gender-role congruity bias did not consistently decrease when decision makers were provided with additional information about those they were rating, but gender-role congruity bias was reduced when information clearly indicated high competence of those being evaluated. Fourth, gender-role congruity bias did not differ between decisions that required comparisons among ratees and decisions made about individual ratees. Fifth, decision makers who were motivated to make careful decisions tended to exhibit less gender-role congruity bias for male-dominated jobs. Finally, for male-dominated jobs, experienced professionals showed smaller gender-role congruity bias than did undergraduates or working adults.", + "citation_count": 560, + "topics": [ + "Gender Diversity and Inequality", + "Social and Intergroup Psychology", + "Work-Family Balance Challenges" + ], + "discovered_via": [ + "neighbor:openalex:W4385952870" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1441180700", + "title": "Regulating Artificial Intelligence Systems: Risks, Challenges, Competencies, and Strategies", + "year": 2015, + "url": "https://doi.org/10.2139/ssrn.2609777", + "doi": "https://doi.org/10.2139/ssrn.2609777", + "abstract": "", + "citation_count": 557, + "topics": [ + "Law, AI, and Intellectual Property", + "Ethics and Social Impacts of AI", + "Digital Transformation in Law" + ], + "discovered_via": [ + "neighbor:openalex:W4400303336" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1501886583", + "title": "The Gender Pay Gap", + "year": 2007, + "url": "https://doi.org/10.5465/amp.2007.24286161", + "doi": "https://doi.org/10.5465/amp.2007.24286161", + "abstract": "Executive Overview The trends in the gender pay gap in the United States form a somewhat mixed picture. On the one hand, after a half a century of stability in the earnings of women relative to men, there has been a substantial increase in women's relative earnings since the late 1970s. One of the things that make this development especially dramatic and significant is that the recent changes contrast markedly with the relative stability of earlier years. On the other hand, there is still a gender pay gap. Women continue to earn considerably less than men on average, and the convergence that began in the late 1970s slowed noticeably in the 1990s. Is this slowdown just a blip in an overall trend, or has the pay gap converged as far as it can? We look at this issue in depth and make some predictions for the future.", + "citation_count": 545, + "topics": [ + "Labor market dynamics and wage inequality", + "Gender, Labor, and Family Dynamics", + "Work-Family Balance Challenges" + ], + "discovered_via": [ + "neighbor:openalex:W4383913712" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1528081447", + "title": "Using data mining to predict secondary school student performance", + "year": 2008, + "url": "http://hdl.handle.net/1822/8024", + "doi": null, + "abstract": "Although the educational level of the Portuguese population has improved in the last decades, the statistics keep Portugal at Europe\u2019s tail end due to its high student failure rates. In particular, lack of success in the core classes of Mathematics and the Portuguese language is extremely serious. On the other hand, the fields of Business Intelligence (BI)/Data Mining (DM), which aim at extracting high-level knowledge from raw data, offer interesting automated tools that can aid the education domain. The present work intends to approach student achievement in secondary education using BI/DM techniques. Recent real-world data (e.g. student grades, demographic, social and school related features) was collected by using school reports and questionnaires. The two core classes (i.e. Mathematics and Portuguese) were modeled under binary/five-level classification and regression tasks. Also, four DM models (i.e. Decision Trees, Random Forest, Neural Networks and Support Vector Machines) and three input selections (e.g. with and without previous grades) were tested. The results show that a good predictive accuracy can be achieved, provided that the first and/or second school period grades are available. Although student achievement is highly influenced by past evaluations, an explanatory analysis has shown that there are also other relevant features (e.g. number of absences, parent\u2019s job and education, alcohol consumption). As a direct outcome of this research, more efficient student prediction tools can be be developed, improving the quality of education and enhancing school resource management.", + "citation_count": 538, + "topics": [ + "Data Mining Algorithms and Applications", + "Online Learning and Analytics", + "Imbalanced Data Classification Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4316038168" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1806647190", + "title": "The Scored Society: Due Process for Automated Predictions", + "year": 2014, + "url": "https://digitalcommons.law.umaryland.edu/fac_pubs/1431", + "doi": null, + "abstract": "Big Data is increasingly mined to rank and rate individuals. Predictive algorithms assess whether we are good credit risks, desirable employees, reliable tenants, valuable customers\u2014or deadbeats, shirkers, menaces, and \u201cwastes of time.\u201d Crucial opportunities are on the line, including the ability to obtain loans, work, housing, and insurance. Though automated scoring is pervasive and consequential, it is also opaque and lacking oversight. In one area where regulation does prevail\u2014credit\u2014the law focuses on credit history, not the derivation of scores from data. Procedural regularity is essential for those stigmatized by \u201cartificially intelligent\u201d scoring systems. The American due process tradition should inform basic safeguards. Regulators should be able to test scoring systems to ensure their fairness and accuracy. Individuals should be granted meaningful opportunities to challenge adverse decisions based on scores miscategorizing them. Without such protections in place, systems could launder biased and arbitrary data into powerfully stigmatizing scores.", + "citation_count": 530, + "topics": [ + "Big Data and Business Intelligence", + "Big Data Technologies and Applications", + "Complex Systems and Decision Making" + ], + "discovered_via": [ + "neighbor:openalex:W4399518864" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2149252982", + "title": "Unequal Representation and Gender Stereotypes in Image Search Results for Occupations", + "year": 2015, + "url": "https://doi.org/10.1145/2702123.2702520", + "doi": "https://doi.org/10.1145/2702123.2702520", + "abstract": "Information environments have the power to affect people's perceptions and behaviors. In this paper, we present the results of studies in which we characterize the gender bias present in image search results for a variety of occupations. We experimentally evaluate the effects of bias in image search results on the images people choose to represent those careers and on people's perceptions of the prevalence of men and women in each occupation. We find evidence for both stereotype exaggeration and systematic underrepresentation of women in search results. We also find that people rate search results higher when they are consistent with stereotypes for a career, and shifting the representation of gender in image search results can shift people's perceptions about real-world distributions. We also discuss tensions between desires for high-quality results and broader societal goals for equality of representation in this space.", + "citation_count": 519, + "topics": [ + "Gender Diversity and Inequality", + "Social and Intergroup Psychology", + "Media, Gender, and Advertising" + ], + "discovered_via": [ + "neighbor:openalex:W4386714740" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1639961155", + "title": "Wasserstein Barycenter and Its Application to Texture Mixing", + "year": 2012, + "url": "https://doi.org/10.1007/978-3-642-24785-9_37", + "doi": "https://doi.org/10.1007/978-3-642-24785-9_37", + "abstract": "", + "citation_count": 502, + "topics": [ + "Generative Adversarial Networks and Image Synthesis", + "Medical Image Segmentation Techniques", + "3D Shape Modeling and Analysis" + ], + "discovered_via": [ + "neighbor:openalex:W4387789492" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2555400967", + "title": "25 Years of Transparency Research: Evidence and Future Directions", + "year": 2016, + "url": "https://doi.org/10.1111/puar.12685", + "doi": "https://doi.org/10.1111/puar.12685", + "abstract": "Abstract This article synthesizes the cross\u2010disciplinary literature on government transparency. It systematically reviews research addressing the topic of government transparency published between 1990 and 2015. The review uses 187 studies to address three questions: (1) What forms of transparency has the literature identified? (2) What outcomes does the literature attribute to transparency? and (3) How successful is transparency in achieving those goals? In addressing these questions, the authors review six interrelated types of transparency and nine governance\u2010 and citizen\u2010related outcomes of transparency. Based on the findings of the analysis, the authors outline an agenda for future research on government transparency and its effects that calls for more systematically investigating the ways in which contextual conditions shape transparency outcomes, replicating studies with varying methodologies, investigating transparency in neglected countries, and paying greater attention to understudied claims of transparency such as improved decision making and management .", + "citation_count": 479, + "topics": [ + "E-Government and Public Services", + "Public Policy and Administration Research", + "Public Procurement and Policy" + ], + "discovered_via": [ + "neighbor:openalex:W4386714740" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W147360723", + "title": "Chapter 39 New developments in models of search in the labor market", + "year": 1999, + "url": "https://doi.org/10.1016/s1573-4463(99)30025-0", + "doi": "https://doi.org/10.1016/s1573-4463(99)30025-0", + "abstract": "", + "citation_count": 475, + "topics": [ + "Labor market dynamics and wage inequality", + "Economic theories and models", + "Firm Innovation and Growth" + ], + "discovered_via": [ + "neighbor:openalex:W4318407310" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1549997466", + "title": "Scripts, plans, and knowledge", + "year": 1975, + "url": "http://ijcai.org/Proceedings/75/Papers/021.pdf", + "doi": null, + "abstract": "We describe a theoretical system intended to facilitate the use of knowledge In an understanding system. The notion of script is introduced to account for knowledge about mundane situations. A program, SAM, is capable of using scripts to understand. The notion of plans is introduced to account for general knowledge about novel situations.", + "citation_count": 461, + "topics": [ + "AI in Service Interactions", + "Advanced Text Analysis Techniques", + "Speech and dialogue systems" + ], + "discovered_via": [ + "neighbor:openalex:W4391830676" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2963844355", + "title": "Stealing Hyperparameters in Machine Learning", + "year": 2018, + "url": "https://doi.org/10.1109/sp.2018.00038", + "doi": "https://doi.org/10.1109/sp.2018.00038", + "abstract": "Hyperparameters are critical in machine learning, as different hyperparameters often result in models with significantly different performance. Hyperparameters may be deemed confidential because of their commercial value and the confidentiality of the proprietary algorithms that the learner uses to learn them. In this work, we propose attacks on stealing the hyperparameters that are learned by a learner. We call our attacks hyperparameter stealing attacks. Our attacks are applicable to a variety of popular machine learning algorithms such as ridge regression, logistic regression, support vector machine, and neural network. We evaluate the effectiveness of our attacks both theoretically and empirically. For instance, we evaluate our attacks on Amazon Machine Learning. Our results demonstrate that our attacks can accurately steal hyperparameters. We also study countermeasures. Our results highlight the need for new defenses against our hyperparameter stealing attacks for certain machine learning algorithms.", + "citation_count": 452, + "topics": [ + "Adversarial Robustness in Machine Learning", + "Machine Learning and Data Classification", + "Anomaly Detection Techniques and Applications" + ], + "discovered_via": [ + "neighbor:openalex:W4387789492" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2103506772", + "title": "Disparities and distrust: The implications of psychological processes for understanding racial disparities in health and health care", + "year": 2008, + "url": "https://doi.org/10.1016/j.socscimed.2008.03.019", + "doi": "https://doi.org/10.1016/j.socscimed.2008.03.019", + "abstract": "", + "citation_count": 452, + "topics": [ + "Racial and Ethnic Identity Research", + "Migration, Health and Trauma", + "Global Health Workforce Issues" + ], + "discovered_via": [ + "neighbor:openalex:W4399518864" + ], + "relevance_score": 0.0, + "relevance_reason": "Concerns racial disparities in healthcare, with no candidate text connecting it to AI or hiring.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W1970200091", + "title": "Usability \u2013 Context, framework, definition, design and evaluation", + "year": 2009, + "url": "https://doi.org/10.1016/j.intcom.2009.04.007", + "doi": "https://doi.org/10.1016/j.intcom.2009.04.007", + "abstract": "For many users the informatics system is essentially the terminal or workstation which they are using, and that is the central computer as they see it. But only too often these users are seen as \u201cend-users\u201d by designers \u2013 and this name may well betray an attitude which causes some of the bad design for users and failures in usability. Designers must see the user as the centre of the computer system instead of as a mere peripheral. This simple concept, easy to state but harder to achieve, is often expounded by ergonomists and human factors specialists. It has been emphasised by Nicholls (1979): In spite of changes in the nature of computing, remnants of old thinking remain with us. In former days, when the CPU was at the heart of a system, designers naturally talked of \u201cterminals\u201d and \u201cperipherals\u201d. I suspect it was in this period that people began to use the term \u201cend user\u201d. The unconscious symbolism is both a symptom and a cause; the \u201cend\u201d user at the \u201cterminal\u201d was often the last person to be considered in the design of the system. It is important to develop a new view of computing systems, and to look at the user in a different light \u2026 taking this view of computing, the centre of a system is the user.", + "citation_count": 421, + "topics": [ + "Information Society and Technology Trends", + "Usability and User Interface Design", + "Information Systems Theories and Implementation" + ], + "discovered_via": [ + "neighbor:openalex:W4407173730" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2137591322", + "title": "Causal effect of intergroup contact on exclusionary attitudes", + "year": 2014, + "url": "https://doi.org/10.1073/pnas.1317670111", + "doi": "https://doi.org/10.1073/pnas.1317670111", + "abstract": "The effect of intergroup contact has long been a question central to social scientists. As political and technological changes bring increased international migration, understanding intergroup contact is increasingly important to scientific and policy debates. Unfortunately, limitations in causal inference using observational data and the practical inability to experimentally manipulate demographic diversity has limited scholars' ability to address the effects of intergroup contact. Here, I report the results of a randomized controlled trial testing the causal effects of repeated intergroup contact, in which Spanish-speaking confederates were randomly assigned to be inserted, for a period of days, into the daily routines of unknowing Anglo-whites living in homogeneous communities in the United States, thus simulating the conditions of demographic change. The result of this experiment is a significant shift toward exclusionary attitudes among treated subjects. This experiment demonstrates that even very minor demographic change causes strong exclusionary reactions. Developed nations and politically liberal subnational units are expected to experience a politically conservative shift as international migration brings increased intergroup contact.", + "citation_count": 399, + "topics": [ + "Social and Intergroup Psychology", + "Electoral Systems and Political Participation", + "Racial and Ethnic Identity Research" + ], + "discovered_via": [ + "neighbor:openalex:W4407762039" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2036262779", + "title": "Challenging \u2018strategic HRM\u2019 and the relevance of the institutional setting", + "year": 2003, + "url": "https://doi.org/10.1111/j.1748-8583.2003.tb00098.x", + "doi": "https://doi.org/10.1111/j.1748-8583.2003.tb00098.x", + "abstract": "One of the more fundamental aspects of the ongoing debate about the added value of HRM relates to \u2018best\u2019 practice versus \u2018best\u2010fit\u2019. Best practice suggests the universal success of certain HR practices, while best\u2010fit acknowledges the relevant impact of contextual factors. We argue that differences in embeddedness and in institutional settings between, for example, countries affect the nature of HRM. To understand this phenomenon, we are in need of additional theory. In this article we will use the theory of new institutionalism as a better way to understand the shaping of HR policies and practices in different settings. After a concise review of the latest debates in the area of strategic HRM, in which the resource\u2010based view is the dominant perspective, we turn to an analysis of HRM in different institutional settings, which suggests the need for additional theory: ie new institutionalism. We offer propositions to explain the impact of different institutional mechanisms, including coercive, normative and mimetic ones, on the shaping of HR policies and practices in organisations. The remainder of the article then focuses on possible implications for practitioners, theoretical implications for future research, and challenges for strategic HRM.", + "citation_count": 387, + "topics": [ + "Corporate Finance and Governance", + "Labor Movements and Unions", + "Management and Organizational Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4383913712" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2132075088", + "title": "Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach", + "year": 2012, + "url": "https://doi.org/10.1016/j.artmed.2012.12.003", + "doi": "https://doi.org/10.1016/j.artmed.2012.12.003", + "abstract": "", + "citation_count": 372, + "topics": [ + "Machine Learning in Healthcare", + "Healthcare Operations and Scheduling Optimization", + "Artificial Intelligence in Healthcare and Education" + ], + "discovered_via": [ + "neighbor:openalex:W4404254054" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2011009054", + "title": "Beyond the user", + "year": 2009, + "url": "https://doi.org/10.1145/1738826.1738829", + "doi": "https://doi.org/10.1145/1738826.1738829", + "abstract": "For many, an interest in Human-Computer Interaction is equivalent to an interest in usability. However, using computers is only one way of relating to them, and only one topic from which we can learn about interactions between people and technology. Here, we focus on not using computers -- ways not to use them, aspects of not using them, what not using them might mean, and what we might learn by examining non-use as seriously as we examine use.", + "citation_count": 369, + "topics": [ + "ICT in Developing Communities", + "Innovative Human-Technology Interaction", + "Smart Cities and Technologies" + ], + "discovered_via": [ + "neighbor:openalex:W4394591980" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1994606570", + "title": "Too much, too little, or just right? Ways explanations impact end users' mental models", + "year": 2013, + "url": "https://doi.org/10.1109/vlhcc.2013.6645235", + "doi": "https://doi.org/10.1109/vlhcc.2013.6645235", + "abstract": "Research is emerging on how end users can correct mistakes their intelligent agents make, but before users can correctly \u201cdebug\u201d an intelligent agent, they need some degree of understanding of how it works. In this paper we consider ways intelligent agents should explain themselves to end users, especially focusing on how the soundness and completeness of the explanations impacts the fidelity of end users' mental models. Our findings suggest that completeness is more important than soundness: increasing completeness via certain information types helped participants' mental models and, surprisingly, their perception of the cost/benefit tradeoff of attending to the explanations. We also found that oversimplification, as per many commercial agents, can be a problem: when soundness was very low, participants experienced more mental demand and lost trust in the explanations, thereby reducing the likelihood that users will pay attention to such explanations at all.", + "citation_count": 367, + "topics": [ + "Explainable Artificial Intelligence (XAI)", + "Spreadsheets and End-User Computing", + "AI in Service Interactions" + ], + "discovered_via": [ + "neighbor:openalex:W4386142022" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2039762259", + "title": "Does Emotional Intelligence Assist in the Prediction of Academic Success?", + "year": 2003, + "url": "https://doi.org/10.1177/0013164403251333", + "doi": "https://doi.org/10.1177/0013164403251333", + "abstract": "Some authors have claimed that emotional intelligence predicts success at work, at school, and in relationships, as well as or better than IQ. Little research exists to support or refute this claim at present. In this study, the ability of emotional intelligence to predict academic achievement was examined in a sample of undergraduate psychology students, using year-end grades as the criterion. The predictive validity of emotional intelligence was compared with the predictive validity of traditional cognitive abilities and the Big Five dimensions of personality. In addition, the incremental predictive validity of each of these three domains was assessed. In this setting, only some measures of Emotional Intelligence predicted academic success, and none of these measures showed incremental predictive validity for academic success over and above cognitive and personality variables. It may be that the overlap between many emotional intelligence measures and traditional measures of intelligence and personality limits their incremental predictive validity in this context.", + "citation_count": 364, + "topics": [ + "Emotional Intelligence and Performance", + "Personality Traits and Psychology", + "Learning Styles and Cognitive Differences" + ], + "discovered_via": [ + "neighbor:openalex:W4409825334" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W266408742", + "title": "Innovation Tournaments: Creating and Selecting Exceptional Opportunities", + "year": 2009, + "url": "http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018955028&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA", + "doi": null, + "abstract": "Managers, entrepreneurs, and venture capitalists all seek to maximize the financial returns from innovation, and profits are driven largely by the quality of the opportunities they pursue. Based on a structured and process-driven approach this book demonstrates how to systematically identify exceptional opportunities for innovation. An innovation tournament, just like its counterpart in sports, starts with a large number of candidates, with opportunities as the players. These opportunities are pitted against each other until only the exceptional survive. This book provides a principled approach for the effective management of innovation tournaments - identifying a wealth of promising opportunities and then evaluating and filtering them intelligently for greatest profitability. With a set of practical tools for creating and identifying new opportunities, it guides the reader in evaluating and screening opportunities. The book demonstrates how to construct an innovation portfolio and how to align the innovation process with an organization's competitive strategy. Innovation Tournaments employs quirky, fresh examples ranging from movies to medical devices. The authors' tool kit is built on their extensive research, their entrepreneurial backgrounds, and their teaching and consulting work with many highly innovative organizations.", + "citation_count": 348, + "topics": [ + "Conferences and Exhibitions Management" + ], + "discovered_via": [ + "neighbor:openalex:W4401533174" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4407173730", + "title": "FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare", + "year": 2025, + "url": "https://doi.org/10.1136/bmj-2024-081554", + "doi": "https://doi.org/10.1136/bmj-2024-081554", + "abstract": "Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. This paper describes the FUTURE-AI framework, which provides guidance for the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI Consortium was founded in 2021 and comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two year period, the FUTURE-AI guideline was established through consensus based on six guiding principles\u2014fairness, universality, traceability, usability, robustness, and explainability. To operationalise trustworthy AI in healthcare, a set of 30 best practices were defined, addressing technical, clinical, socioethical, and legal dimensions. The recommendations cover the entire lifecycle of healthcare AI, from design, development, and validation to regulation, deployment, and monitoring.", + "citation_count": 344, + "topics": [ + "Artificial Intelligence in Healthcare and Education", + "Ethics in Clinical Research", + "Artificial Intelligence in Healthcare" + ], + "discovered_via": [ + "query:AI recruitment system human in the loop recruiter decision support workflow architecture monitoring" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2124861786", + "title": "Recommendations for Biomarker Identification and Qualification in Clinical Proteomics", + "year": 2010, + "url": "https://doi.org/10.1126/scitranslmed.3001249", + "doi": "https://doi.org/10.1126/scitranslmed.3001249", + "abstract": "Clinical proteomics has yielded some early positive results-the identification of potential disease biomarkers-indicating the promise for this analytical approach to improve the current state of the art in clinical practice. However, the inability to verify some candidate molecules in subsequent studies has led to skepticism among many clinicians and regulatory bodies, and it has become evident that commonly encountered shortcomings in fundamental aspects of experimental design mainly during biomarker discovery must be addressed in order to provide robust data. In this Perspective, we assert that successful studies generally use suitable statistical approaches for biomarker definition and confirm results in independent test sets; in addition, we describe a brief set of practical and feasible recommendations that we have developed for investigators to properly identify and qualify proteomic biomarkers, which could also be used as reporting requirements. Such recommendations should help put proteomic biomarker discovery on the solid ground needed for turning the old promise into a new reality.", + "citation_count": 335, + "topics": [ + "Advanced Proteomics Techniques and Applications", + "Advanced Biosensing Techniques and Applications", + "Statistical Methods in Clinical Trials" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4391830676", + "title": "Generative artificial intelligence in innovation management: A preview of future research developments", + "year": 2024, + "url": "https://doi.org/10.1016/j.jbusres.2024.114542", + "doi": "https://doi.org/10.1016/j.jbusres.2024.114542", + "abstract": "This study outlines the future research opportunities related to Generative Artificial Intelligence (GenAI) in innovation management. To this end, it combines a review of the academic literature with the results of a Delphi study involving leading innovation management scholars. Ten major research themes emerged that can guide future research developments at the intersection of GenAI and innovation management: 1) Gen AI and innovation types; 2) GenAI, dominant designs and technology evolution; 3) Scientific and artistic creativity and GenAI-enabled innovations; 4) GenAI-enabled innovations and intellectual property; 5) GenAI and new product development; 6) Multimodal/unimodal GenAI and innovation outcomes; 7) GenAI, agency and ecosystems; 8) Policymakers, lawmakers and anti-trust authorities in the regulation of GenAI-enabled innovation; 9) Misuse and unethical use of GenAI leading to biased innovation; and 10) Organizational design and boundaries for GenAI-enabled innovation. The paper concludes by discussing how these themes can inform theoretical development in innovation management studies.", + "citation_count": 327, + "topics": [ + "Big Data and Business Intelligence", + "AI in Service Interactions", + "Digital Transformation in Industry" + ], + "discovered_via": [ + "query:generative AI recruiting systems candidate communication job descriptions interview assistants" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W324102958", + "title": "The $2.6 Billion Pill \u2014 Methodologic and Policy Considerations", + "year": 2015, + "url": "https://doi.org/10.1056/nejmp1500848", + "doi": "https://doi.org/10.1056/nejmp1500848", + "abstract": "Interview with Dr. Jerry Avorn on a new estimate of the cost of bringing a drug to market. (12:09)Download An industry-supported center claims that it costs companies $2.6 billion to develop a new drug. But we need a broader-based and more transparent reckoning of research-and-development costs to inform discussions about fostering innovation and paying for medications.", + "citation_count": 323, + "topics": [ + "Biotechnology and Related Fields", + "Pharmaceutical Economics and Policy", + "Biomedical Ethics and Regulation" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W157856474", + "title": "Ethics and HRM: A Review and Conceptual Analysis", + "year": 2002, + "url": "https://doi.org/10.1023/a:1014090411946", + "doi": "https://doi.org/10.1023/a:1014090411946", + "abstract": "", + "citation_count": 317, + "topics": [ + "Ethics in Business and Education", + "Ethics in medical practice", + "Management and Organizational Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4390315357", + "title": "The Robots Are Here: Navigating the Generative AI Revolution in Computing Education", + "year": 2023, + "url": "https://doi.org/10.1145/3623762.3633499", + "doi": "https://doi.org/10.1145/3623762.3633499", + "abstract": "Recent advancements in artificial intelligence (AI) and specifically generative AI (GenAI) are threatening to fundamentally reshape computing and society. Largely driven by large language models (LLMs), many tools are now able to interpret and generate both natural language instructions and source code. These capabilities have sparked urgent questions in the computing education community around how educators should adapt their pedagogy to address the challenges and to leverage the opportunities presented by this new technology. In this working group report, we undertake a comprehensive exploration of generative AI in the context of computing education and make five significant contributions. First, we provide a detailed review of the literature on LLMs in computing education and synthesise findings from 71 primary articles, nearly 80% of which have been published in the first 8 months of 2023. Second, we report the findings of a survey of computing students and instructors from across 20 countries, capturing prevailing attitudes towards GenAI/LLMs and their use in computing education contexts. Third, to understand how pedagogy is already changing, we offer insights collected from in-depth interviews with 22 computing educators from five continents. Fourth, we use the ACM Code of Ethics to frame a discussion of ethical issues raised by the use of large language models in computing education, and we provide concrete advice for policy makers, educators, and students. Finally, we benchmark the performance of several current GenAI models/tools on various computing education datasets, and highlight the extent to which the capabilities of current models are rapidly improving.", + "citation_count": 313, + "topics": [ + "Teaching and Learning Programming", + "Online Learning and Analytics", + "Scientific Computing and Data Management" + ], + "discovered_via": [ + "query:generative AI recruiting systems candidate communication job descriptions interview assistants" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1547466200", + "title": "Using Sampling and Queries to Extract Rules from Trained Neural Networks", + "year": 1994, + "url": "https://doi.org/10.1016/b978-1-55860-335-6.50013-1", + "doi": "https://doi.org/10.1016/b978-1-55860-335-6.50013-1", + "abstract": "", + "citation_count": 311, + "topics": [ + "Machine Learning and Algorithms", + "Machine Learning and Data Classification", + "AI-based Problem Solving and Planning" + ], + "discovered_via": [ + "neighbor:openalex:W4386142022" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2118646792", + "title": "Creating and managing value in collaborative networks", + "year": 2004, + "url": "https://doi.org/10.1108/09600030410533574", + "doi": "https://doi.org/10.1108/09600030410533574", + "abstract": "This is a theoretical paper that examines the interplay between individual and collective capabilities and competencies and value transactions in collaborative environments. The theory behind value creation is examined and two types of value are identified, internal value (shareholder value) and external value (value proposition). The literature on collaborative enterprises/network is also examined with particular emphasis on supply chains, extended/virtual enterprises and clusters as representatives of different forms and maturities of collaboration. The interplay of value transactions and competencies and capabilities are examined and discussed in detail. Finally, a model is presented which consists of value transactions and a table that compares the characteristics of different types of collaborative enterprises/networks. It is proposed that this model presents a platform for further research to develop an in\u2010depth understanding into how value may be created and managed in collaborative enterprises/networks.", + "citation_count": 306, + "topics": [ + "Collaboration in agile enterprises", + "Business Strategy and Innovation", + "Innovation and Knowledge Management" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1559225021", + "title": "Chapter 13 The economic analysis of labor market discrimination: A survey", + "year": 1986, + "url": "https://doi.org/10.1016/s1573-4463(86)01016-7", + "doi": "https://doi.org/10.1016/s1573-4463(86)01016-7", + "abstract": "", + "citation_count": 305, + "topics": [ + "Labor market dynamics and wage inequality", + "Global trade and economics", + "Employment and Welfare Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4386714740" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3004853109", + "title": "Reducing Exclusionary Attitudes through Interpersonal Conversation: Evidence from Three Field Experiments", + "year": 2020, + "url": "https://doi.org/10.1017/s0003055419000923", + "doi": "https://doi.org/10.1017/s0003055419000923", + "abstract": "Exclusionary attitudes\u2014prejudice toward outgroups and opposition to policies that promote their well-being\u2014are presenting challenges to democratic societies worldwide. Drawing on insights from psychology, we argue that non-judgmentally exchanging narratives in interpersonal conversations can facilitate durable reductions in exclusionary attitudes. We support this argument with evidence from three pre-registered field experiments targeting exclusionary attitudes toward unauthorized immigrants and transgender people. In these experiments, 230 canvassers conversed with 6,869 voters across 7 US locations. In Experiment 1, face-to-face conversations deploying arguments alone had no effects on voters\u2019 exclusionary immigration policy or prejudicial attitudes, but otherwise identical conversations also including the non-judgmental exchange of narratives durably reduced exclusionary attitudes for at least four months (d = 0.08). Experiments 2 and 3, targeting transphobia, replicate these findings and support the scalability of this strategy (ds = 0.08, 0.04). Non-judgmentally exchanging narratives can help overcome the resistance to persuasion often encountered in discussions of these contentious topics.", + "citation_count": 304, + "topics": [ + "Social and Intergroup Psychology", + "Media Influence and Health", + "Social Media and Politics" + ], + "discovered_via": [ + "neighbor:openalex:W4407762039" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1986943801", + "title": "Designing eHealth that Matters via a Multidisciplinary Requirements Development Approach", + "year": 2013, + "url": "https://doi.org/10.2196/resprot.2547", + "doi": "https://doi.org/10.2196/resprot.2547", + "abstract": "BACKGROUND: Requirements development is a crucial part of eHealth design. It entails all the activities devoted to requirements identification, the communication of requirements to other developers, and their evaluation. Currently, a requirements development approach geared towards the specifics of the eHealth domain is lacking. This is likely to result in a mismatch between the developed technology and end user characteristics, physical surroundings, and the organizational context of use. It also makes it hard to judge the quality of eHealth design, since it makes it difficult to gear evaluations of eHealth to the main goals it is supposed to serve. OBJECTIVE: In order to facilitate the creation of eHealth that matters, we present a practical, multidisciplinary requirements development approach which is embedded in a holistic design approach for eHealth (the Center for eHealth Research roadmap) that incorporates both human-centered design and business modeling. METHODS: Our requirements development approach consists of five phases. In the first, preparatory, phase the project team is composed and the overall goal(s) of the eHealth intervention are decided upon. Second, primary end users and other stakeholders are identified by means of audience segmentation techniques and our stakeholder identification method. Third, the designated context of use is mapped and end users are profiled by means of requirements elicitation methods (eg, interviews, focus groups, or observations). Fourth, stakeholder values and eHealth intervention requirements are distilled from data transcripts, which leads to phase five, in which requirements are communicated to other developers using a requirements notation template we developed specifically for the context of eHealth technologies. RESULTS: The end result of our requirements development approach for eHealth interventions is a design document which includes functional and non-functional requirements, a list of stakeholder values, and end user profiles in the form of personas (fictitious end users, representative of a primary end user group). CONCLUSIONS: The requirements development approach presented in this article enables eHealth developers to apply a systematic and multi-disciplinary approach towards the creation of requirements. The cooperation between health, engineering, and social sciences creates a situation in which a mismatch between design, end users, and the organizational context can be avoided. Furthermore, we suggest to evaluate eHealth on a feature-specific level in order to learn exactly why such a technology does or does not live up to its expectations.", + "citation_count": 300, + "topics": [ + "Persona Design and Applications", + "Persona Design and Applications", + "Digital Mental Health Interventions" + ], + "discovered_via": [ + "neighbor:openalex:W4407173730" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1980898411", + "title": "Reviewing Diversity Training: Where We Have Been and Where We Should Go", + "year": 2012, + "url": "https://doi.org/10.5465/amle.2008.0090", + "doi": "https://doi.org/10.5465/amle.2008.0090", + "abstract": "We review and critically examine 178 articles whose authors have investigated numerous aspects of diversity training programs on campuses and in the workplace. We first examine the characteristics of the research, including sample, study method, and theoretical framework. Consistent with the training framework of Baldwin and Ford (1988) and Blume and colleagues (2010), we then organize the articles by the context of training, training design, trainees' characteristics, and training outputs. Although we found a myriad of different forms, shapes, and combinations of diversity training in terms of its design elements, some programs (e.g., integrated training) were relatively rare, yet authors viewed them more positively than other programs (e.g., stand-alone training). We discuss gaps in the literature and provide suggestions for future research on diversity training.", + "citation_count": 298, + "topics": [ + "Gender Diversity and Inequality", + "Higher Education Research Studies", + "Mentoring and Academic Development" + ], + "discovered_via": [ + "neighbor:openalex:W4385952870" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4399518864", + "title": "The impact of generative artificial intelligence on socioeconomic inequalities and policy making", + "year": 2024, + "url": "https://doi.org/10.1093/pnasnexus/pgae191", + "doi": "https://doi.org/10.1093/pnasnexus/pgae191", + "abstract": "Abstract Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section, we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI.", + "citation_count": 290, + "topics": [ + "Ethics and Social Impacts of AI", + "AI in Service Interactions" + ], + "discovered_via": [ + "query:algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2028877361", + "title": "ARE WE GETTING FOOLED AGAIN? COMING TO TERMS WITH LIMITATIONS IN THE USE OF PERSONALITY TESTS FOR PERSONNEL SELECTION", + "year": 2007, + "url": "https://doi.org/10.1111/j.1744-6570.2007.00100.x", + "doi": "https://doi.org/10.1111/j.1744-6570.2007.00100.x", + "abstract": "We recently published an article in which we highlighted a number of issues associated with the use of self\u2010report personality tests in personnel selection contexts ( Morgeson et al., 2007 ). Both Ones, Dilchert, Viswesvaran, and Judge (2007) and Tett and Christiansen (2007) have written responses to this article. In our response to these articles we address many of the issues raised by Ones et al. and Tett and Christiansen. In addition to a detailed response, we make the following 4 key points: (1) Our criticisms of personality testing apply only to the selection context, not to all research on personality; (2) the observed validities of personality tests predicting job performance criteria are low and have not changed much over time; (3) when evaluating the usefulness of using personality tests to select applicants, one must not ignore the observed, uncorrected validity; and (4) when discussing the value of personality tests for selection contexts, the most important criteria are those that reflect job performance. Implications for personality testing research and practice are discussed.", + "citation_count": 289, + "topics": [ + "Personality Traits and Psychology", + "Employer Branding and e-HRM", + "Medical Education and Admissions" + ], + "discovered_via": [ + "neighbor:openalex:W4385952870" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2612890464", + "title": "Standardizing Ethical Design for Artificial Intelligence and Autonomous Systems", + "year": 2017, + "url": "https://doi.org/10.1109/mc.2017.154", + "doi": "https://doi.org/10.1109/mc.2017.154", + "abstract": "AI is here now, available to anyone with access to digital technology and the Internet. But its consequences for our social order aren't well understood. How can we guide the way technology impacts society?", + "citation_count": 280, + "topics": [ + "Ethics and Social Impacts of AI" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4316038168", + "title": "Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods", + "year": 2023, + "url": "https://doi.org/10.3390/bdcc7010015", + "doi": "https://doi.org/10.3390/bdcc7010015", + "abstract": "One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfairness and bias. This study examines the current knowledge on bias and unfairness in machine learning models. The systematic review followed the PRISMA guidelines and is registered on OSF plataform. The search was carried out between 2021 and early 2022 in the Scopus, IEEE Xplore, Web of Science, and Google Scholar knowledge bases and found 128 articles published between 2017 and 2022, of which 45 were chosen based on search string optimization and inclusion and exclusion criteria. We discovered that the majority of retrieved works focus on bias and unfairness identification and mitigation techniques, offering tools, statistical approaches, important metrics, and datasets typically used for bias experiments. In terms of the primary forms of bias, data, algorithm, and user interaction were addressed in connection to the preprocessing, in-processing, and postprocessing mitigation methods. The use of Equalized Odds, Opportunity Equality, and Demographic Parity as primary fairness metrics emphasizes the crucial role of sensitive attributes in mitigating bias. The 25 datasets chosen span a wide range of areas, including criminal justice image enhancement, finance, education, product pricing, and health, with the majority including sensitive attributes. In terms of tools, Aequitas is the most often referenced, yet many of the tools were not employed in empirical experiments. A limitation of current research is the lack of multiclass and multimetric studies, which are found in just a few works and constrain the investigation to binary-focused method. Furthermore, the results indicate that different fairness metrics do not present uniform results for a given use case, and that more research with varied model architectures is necessary to standardize which ones are more appropriate for a given context. We also observed that all research addressed the transparency of the algorithm, or its capacity to explain how decisions are taken.", + "citation_count": 273, + "topics": [ + "Ethics and Social Impacts of AI", + "Explainable Artificial Intelligence (XAI)", + "Privacy-Preserving Technologies in Data" + ], + "discovered_via": [ + "query:AI hiring methods causal evaluation field experiment longitudinal quality of hire" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2059141064", + "title": "k-NN as an implementation of situation testing for discrimination discovery and prevention", + "year": 2011, + "url": "https://doi.org/10.1145/2020408.2020488", + "doi": "https://doi.org/10.1145/2020408.2020488", + "abstract": "With the support of the legally-grounded methodology of situation testing, we tackle the problems of discrimination discovery and prevention from a dataset of historical decisions by adopting a variant of k-NN classification. A tuple is labeled as discriminated if we can observe a significant difference of treatment among its neighbors belonging to a protected-by-law group and its neighbors not belonging to it. Discrimination discovery boils down to extracting a classification model from the labeled tuples. Discrimination prevention is tackled by changing the decision value for tuples labeled as discriminated before training a classifier. The approach of this paper overcomes legal weaknesses and technical limitations of existing proposals.", + "citation_count": 273, + "topics": [ + "Ethics and Social Impacts of AI", + "Computational and Text Analysis Methods", + "Explainable Artificial Intelligence (XAI)" + ], + "discovered_via": [ + "neighbor:openalex:W4387789492" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1488955142", + "title": "A Matter of Context: A Meta\u2010Analytic Investigation of the Relative Validity of Contextualized and Noncontextualized Personality Measures", + "year": 2012, + "url": "https://doi.org/10.1111/j.1744-6570.2012.01250.x", + "doi": "https://doi.org/10.1111/j.1744-6570.2012.01250.x", + "abstract": "The empirical evidence that has accumulated in support of the notion that personality is a valid predictor of employee performance is vast, yet debate on the matter continues. This study investigates frame\u2010of\u2010reference effects as they relate to the validity of self\u2010report measures of personality. Specifically, we compare the validities of general, noncontextualized personality measures and work\u2010specific, contextualized measures. The findings suggest that personality measures are a more valid predictor of performance when the scale items or instructions are framed specifically so as to reference work\u2010specific behaviors. We found that the validities for noncontextualized measures of personality ranged from .02 to .22, with a mean validity of .11. The validities for contextualized measures ranged from .14 to .30, with a mean of .24 . Additional moderator analyses were conducted in an effort to examine several alternate explanations for these validity differences. Specifically, we examined differences between the developmental purpose (general use vs. workplace use) and reliabilities of each type of personality measure. We also compared the validities from published studies to those from unpublished studies. Results suggest that these moderators did not have an impact on the validity differences between noncontextualized and contextualized measures.", + "citation_count": 262, + "topics": [ + "Personality Traits and Psychology", + "Job Satisfaction and Organizational Behavior", + "Ego Development and Educational Practices" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1989277811", + "title": "Mechanical versus clinical data combination in selection and admissions decisions: A meta-analysis.", + "year": 2013, + "url": "https://doi.org/10.1037/a0034156", + "doi": "https://doi.org/10.1037/a0034156", + "abstract": "In employee selection and academic admission decisions, holistic (clinical) data combination methods continue to be relied upon and preferred by practitioners in our field. This meta-analysis examined and compared the relative predictive power of mechanical methods versus holistic methods in predicting multiple work (advancement, supervisory ratings of performance, and training performance) and academic (grade point average) criteria. There was consistent and substantial loss of validity when data were combined holistically-even by experts who are knowledgeable about the jobs and organizations in question-across multiple criteria in work and academic settings. In predicting job performance, the difference between the validity of mechanical and holistic data combination methods translated into an improvement in prediction of more than 50%. Implications for evidence-based practice are discussed.", + "citation_count": 259, + "topics": [ + "Medical Education and Admissions", + "Patient Satisfaction in Healthcare", + "Psychometric Methodologies and Testing" + ], + "discovered_via": [ + "neighbor:openalex:W4385952870" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W327258225", + "title": "Epistemology and the Psychology of Human Judgment", + "year": 2005, + "url": "https://doi.org/10.1093/0195162293.001.0001", + "doi": "https://doi.org/10.1093/0195162293.001.0001", + "abstract": "Abstract This book presents a new approach to epistemology (the theory of human knowledge and reasoning). Its approach aims to liberate epistemology from the scholastic debates of standard analytic epistemology, and treat it as a branch of the philosophy of science. The approach is novel in its use of cost-benefit analysis to guide people facing real reasoning problems and in its framework for resolving normative disputes in psychology. Based on empirical data, the book shows how people can improve their reasoning by relying on Statistical Prediction Rules (SPRs). It then develops and articulates the positive core of the book. The view presented \u2014 Strategic Reliabilism \u2014 claims that epistemic excellence consists in the efficient allocation of cognitive resources to reliable reasoning strategies, applied to significant problems. The last third of the book develops the implications of this view for standard analytic epistemology; for resolving normative disputes in psychology; and for offering practical, concrete advice on how this theory can improve real people's reasoning.", + "citation_count": 254, + "topics": [ + "Epistemology, Ethics, and Metaphysics", + "Philosophy and History of Science", + "Decision-Making and Behavioral Economics" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2070125214", + "title": "Do patients trust computers?", + "year": 2006, + "url": "https://doi.org/10.1002/bdm.542", + "doi": "https://doi.org/10.1002/bdm.542", + "abstract": "Abstract In two studies, we inquired whether patients accept medical recommendations that come from a computer program rather than from a physician. In study 1, we found that subjects, when deciding whether to have an operation or not in different medical scenarios, were more likely to follow a recommendation that came from a physician than one that came from a computer program. Subjects stated that they would feel less responsible when following a recommendation than when deciding against it. Following a physician's recommendation reduced the feeling of responsibility more than following that of a computer program. The difference in feeling of responsibility when following versus when not following a recommendation partly mediated subjects' inclination to follow the physician more. In our second study, we found that subjects were more decision seeking when they received a recommendation or decision from a computer program, and they were more decision seeking when they had to accept a decision than when they received a recommendation. Subjects also trusted the physician more than the computer program to make a good recommendation or decision. Copyright \u00a9 2006 John Wiley & Sons, Ltd.", + "citation_count": 247, + "topics": [ + "Patient-Provider Communication in Healthcare", + "Decision-Making and Behavioral Economics", + "Clinical Reasoning and Diagnostic Skills" + ], + "discovered_via": [ + "neighbor:openalex:W4399518864" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1979403545", + "title": "The envisioning cards", + "year": 2012, + "url": "https://doi.org/10.1145/2207676.2208562", + "doi": "https://doi.org/10.1145/2207676.2208562", + "abstract": "We introduce the Envisioning Cards - a versatile toolkit for attending to human values during design processes - and discuss their early use. Drawing on almost twenty years of work in value sensitive design, the Envisioning Cards are built upon a set of four envisioning criteria: stakeholders, time, values, and pervasiveness. Each card contains on one side a title and an evocative image related to the card theme; on the flip side, the card shows the envisioning criterion, elaborates on the theme, and provides a focused design activity. Reports from the field demonstrate use in a range of research and design activities including ideation, co-design, heuristic critique, and more.", + "citation_count": 236, + "topics": [ + "Innovative Human-Technology Interaction", + "Design Education and Practice", + "Service and Product Innovation" + ], + "discovered_via": [ + "neighbor:openalex:W4392182430" + ], + "relevance_score": 0.0, + "relevance_reason": "A general value-sensitive design toolkit with no AI hiring application and outside the publication window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W1980773669", + "title": "Determinants of Overconfidence and Miscalibration: The Roles of Random Error and Ecological Structure", + "year": 1996, + "url": "https://doi.org/10.1006/obhd.1996.0011", + "doi": "https://doi.org/10.1006/obhd.1996.0011", + "abstract": "", + "citation_count": 236, + "topics": [ + "Decision-Making and Behavioral Economics", + "Forecasting Techniques and Applications", + "Social and Intergroup Psychology" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2097914836", + "title": "Detecting the historical roots of research fields by reference publication year spectroscopy (RPYS)", + "year": 2013, + "url": "https://doi.org/10.1002/asi.23089", + "doi": "https://doi.org/10.1002/asi.23089", + "abstract": "We introduce the quantitative method named \u201cReference Publication Year Spectroscopy\u201d ( RPYS ). With this method one can determine the historical roots of research fields and quantify their impact on current research. RPYS is based on the analysis of the frequency with which references are cited in the publications of a specific research field in terms of the publication years of these cited references. The origins show up in the form of more or less pronounced peaks mostly caused by individual publications that are cited particularly frequently. In this study, we use research on graphene and on solar cells to illustrate how RPYS functions, and what results it can deliver.", + "citation_count": 224, + "topics": [ + "Geochemistry and Geologic Mapping", + "History and advancements in chemistry", + "Isotope Analysis in Ecology" + ], + "discovered_via": [ + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2055112475", + "title": "A Human Activity Approach to User Interfaces", + "year": 1989, + "url": "https://doi.org/10.1207/s15327051hci0403_1", + "doi": "https://doi.org/10.1207/s15327051hci0403_1", + "abstract": "How can we understand why a bank teller has different needs for a user interface than those of casual users of a machine teller, or why a graphic designer needs a different user interface than a secretary? This article presents a framework for the design of user interfaces that originates from the work situations in which computer-based artifacts are used: The framework deals with the role of the user interface in purposeful human work. Human activity theory is used in this analysis. The purpose of this article is to make the reader curious and hopefully open his or her eyes to a somewhat different way of thinking about the user interface. The article applies examples of real-life interfaces to support this process, but it does not include a systematic presentation of empirical results. I focus on the role of the computer application in use. Thus, it is necessary to consider human-computer interaction and other related work conditions. I deal with human experience and competence as being rooted in the practice of the group that conducts the specific work activity. The main conclusions are: The user interface cannot be seen independently of the use activity (i.e., the professional, socially organized practice of the users and the material conditions for the activity, including the object of the activity). The standard view in these situations is to deduce an ultimate set of operations from an abstract use activity and apply these to design and analysis. This article argues that the user interface fully reveals itself to us only when in use. What is a good user interface for those with a certain degree of competence may not be efficient for those with different levels of competence. I give certain general recommendations for the user interface, but I have no guarantee that such recommendations are applicable to the specific case wherein these concerns may be overruled by specific social or material concerns.", + "citation_count": 223, + "topics": [ + "Usability and User Interface Design", + "Information Systems Theories and Implementation" + ], + "discovered_via": [ + "neighbor:openalex:W4394591980" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2349201769", + "title": "Study on ABCD Analysis Technique for Business Models, Business Strategies, Operating Concepts & Business Systems", + "year": 2015, + "url": "https://doi.org/10.5281/zenodo.161137", + "doi": "https://doi.org/10.5281/zenodo.161137", + "abstract": "", + "citation_count": 221, + "topics": [ + "Business Strategies and Innovation", + "Big Data and Business Intelligence", + "Technology and Data Analysis" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4401533174", + "title": "The Crowdless Future? Generative AI and Creative Problem-Solving", + "year": 2024, + "url": "https://doi.org/10.1287/orsc.2023.18430", + "doi": "https://doi.org/10.1287/orsc.2023.18430", + "abstract": "The rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative problem-solving through human-guided AI partnerships. To explore this potential, we initiated a crowdsourcing challenge focused on sustainable, circular economy business ideas generated by the human crowd (HC) and collaborative human-AI efforts using two alternative forms of solution search. The challenge attracted 125 global solvers from various industries, and we used strategic prompt engineering to generate the human-AI solutions. We recruited 300 external human evaluators to judge a randomized selection of 13 out of 234 solutions, totaling 3,900 evaluator-solution pairs. Our results indicate that while human crowd solutions exhibited higher novelty\u2014both on average and for highly novel outcomes\u2014human-AI solutions demonstrated superior strategic viability, financial and environmental value, and overall quality. Notably, human-AI solutions cocreated through differentiated search, where human-guided prompts instructed the large language model to sequentially generate outputs distinct from previous iterations, outperformed solutions generated through independent search. By incorporating \u201cAI in the loop\u201d into human-centered creative problem-solving, our study demonstrates a scalable, cost-effective approach to augment the early innovation phases and lays the groundwork for investigating how integrating human-AI solution search processes can drive more impactful innovations. Funding: This work was supported by Harvard Business School (Division of Research and Faculty Development) and the Laboratory for Innovation Science at Harvard (LISH) at the Digital Data and Design (D 3 ) Institute at Harvard. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.18430 .", + "citation_count": 219, + "topics": [ + "Open Source Software Innovations", + "Mobile Crowdsensing and Crowdsourcing", + "Private Equity and Venture Capital" + ], + "discovered_via": [ + "query:AI hiring methods causal evaluation field experiment longitudinal quality of hire" + ], + "relevance_score": 0.0, + "relevance_reason": "Evaluates generative AI for creative problem-solving and innovation, not hiring processes.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4391974599", + "title": "Generative AI for Transformative Healthcare: A Comprehensive Study of Emerging Models, Applications, Case Studies, and Limitations", + "year": 2024, + "url": "https://doi.org/10.1109/access.2024.3367715", + "doi": "https://doi.org/10.1109/access.2024.3367715", + "abstract": "Generative artificial intelligence (GAI) can be broadly described as an artificial intelligence system capable of generating images, text, and other media types with human prompts. GAI models like ChatGPT, DALL-E, and Bard have recently caught the attention of industry and academia equally. GAI applications span various industries like art, gaming, fashion, and healthcare. In healthcare, GAI shows promise in medical research, diagnosis, treatment, and patient care and is already making strides in real-world deployments. There has yet to be any detailed study concerning the applications and scope of GAI in healthcare. Addressing this research gap, we explore several applications, real-world scenarios, and limitations of GAI in healthcare. We examine how GAI models like ChatGPT and DALL-E can be leveraged to aid in the applications of medical imaging, drug discovery, personalized patient treatment, medical simulation and training, clinical trial optimization, mental health support, healthcare operations and research, medical chatbots, human movement simulation, and a few more applications. Along with applications, we cover four real-world healthcare scenarios that employ GAI: visual snow syndrome diagnosis, molecular drug optimization, medical education, and dentistry. We also provide an elaborate discussion on seven healthcare-customized LLMs like Med-PaLM, BioGPT, DeepHealth, etc.,Since GAI is still evolving, it poses challenges like the lack of professional expertise in decision making, risk of patient data privacy, issues in integrating with existing healthcare systems, and the problem of data bias which are elaborated on in this work along with several other challenges. We also put forward multiple directions for future research in GAI for healthcare.", + "citation_count": 218, + "topics": [ + "Artificial Intelligence in Healthcare and Education", + "Biomedical and Engineering Education", + "Machine Learning in Healthcare" + ], + "discovered_via": [ + "query:AI recruitment system human in the loop recruiter decision support workflow architecture monitoring" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W39460904", + "title": "A Kantian Theory of Meaningful Work", + "year": 1998, + "url": "https://doi.org/10.1023/a:1006023500585", + "doi": "https://doi.org/10.1023/a:1006023500585", + "abstract": "", + "citation_count": 216, + "topics": [ + "Transport and Economic Policies", + "Political Influence and Corporate Strategies" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2080013315", + "title": "The application of artificial intelligence in auditing: Looking back to the future", + "year": 2012, + "url": "https://doi.org/10.1016/j.eswa.2012.01.098", + "doi": "https://doi.org/10.1016/j.eswa.2012.01.098", + "abstract": "", + "citation_count": 211, + "topics": [ + "Auditing, Earnings Management, Governance", + "Stock Market Forecasting Methods" + ], + "discovered_via": [ + "neighbor:openalex:W4407173730" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3169835994", + "title": "Persuasion Knowledge in the Marketplace: A Meta\u2010Analysis", + "year": 2021, + "url": "https://doi.org/10.1002/jcpy.1258", + "doi": "https://doi.org/10.1002/jcpy.1258", + "abstract": "Since the introduction of the persuasion knowledge model more than 25 years ago, many research studies have investigated how consumers\u2019 persuasion knowledge affects their reactions to persuasion attempts. While most results have shown that persuasion knowledge increases coping responses and leads to less favorable evaluations of marketer actions, the findings vary considerably, leaving researchers with a limited understanding of the substance and structure of persuasion knowledge effects and the conditions that explain their variability. To develop a better understanding of persuasion knowledge effects in the marketplace, this study builds on the concept of persuasion to predict responses to marketers\u2019 attempts to persuade consumers with different levels of persuasion knowledge. The study presents a meta\u2010analysis of the findings in 148 papers and 171 distinct data sets. Persuasion knowledge effects can be viewed as substantial compared with persuasion attempts, but persuasion knowledge cannot suppress or eliminate persuasion effects in the marketplace, as it only reaches around 50% of the explanatory power of persuasion. Persuasion knowledge effects on evaluations and coping depend on the characteristics of the persuasion process. All persuasion elements that help consumers identify and better understand benefits not just for themselves, but also for marketers and how marketers realize their benefits\u2014such as the use of personal communication, communication about unfamiliar products or products with experience attributes, and receiver experience\u2014lead to less favorable effects for marketers. This paper\u2019s insights provide a new framework for persuasion knowledge effects in the marketplace, ideas for future research, and implications for researchers, consumers, policymakers, and marketers.", + "citation_count": 199, + "topics": [ + "Consumer Behavior in Brand Consumption and Identification", + "Digital Marketing and Social Media", + "Media Influence and Health" + ], + "discovered_via": [ + "neighbor:openalex:W4407762039" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1872649730", + "title": "An Introduction to Artificial Intelligence in Behavioral and Mental Health Care", + "year": 2015, + "url": "https://doi.org/10.1016/b978-0-12-420248-1.00001-5", + "doi": "https://doi.org/10.1016/b978-0-12-420248-1.00001-5", + "abstract": "", + "citation_count": 199, + "topics": [ + "Functional Brain Connectivity Studies", + "Digital Mental Health Interventions", + "Artificial Intelligence in Healthcare and Education" + ], + "discovered_via": [ + "neighbor:openalex:W4400303336" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2299025542", + "title": "A New ABCD Technique to Analyze Business Models & Concepts", + "year": 2015, + "url": "https://doi.org/10.5281/zenodo.61652", + "doi": "https://doi.org/10.5281/zenodo.61652", + "abstract": "Various techniques are used to analyze individual characteristics or organizational effectiveness like SWOT analysis, SWOC analysis, PEST analysis etc. These techniques provide an easy and systematic way of identifying various issues affecting a system and provides opportunity for further development. Whereas these provide a broad based assessment for individual institutions and systems, it suffers limitations while applying to business context. The success of any business model depends on how a company makes profit by specifying its position in the value chain. Therefore identifying suitable business models which would enhance customer value and generate revenue is the current challenge for organizations. In this paper, we have proposed a new technique namely ABCD Technique to analyze a business model and to study its effectiveness in providing value to its stake holders and sustainable profit through expected revenue generation. ABCD is an acronym that stands for Advantages, Benefits, Constraints, and Disadvantages. Application of ABCD analysis results in an organized list of business advantages, benefits, constraints, and disadvantages in a systematic matrix. The entire framework is divided under various issues/area of focus and various business deployment factors affecting the business/concept can be identified and analyzed under each issues by identifying critical effective elements. This analyzing technique being simple, gives guideline to identify and analyze the effectiveness of any business model and new concepts developed.", + "citation_count": 186, + "topics": [ + "Innovations and Analysis in Business and Education" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1973704684", + "title": "Implementing IT Service Management: A systematic literature review", + "year": 2013, + "url": "https://doi.org/10.1016/j.ijinfomgt.2013.01.004", + "doi": "https://doi.org/10.1016/j.ijinfomgt.2013.01.004", + "abstract": "", + "citation_count": 182, + "topics": [ + "Information Technology Governance and Strategy", + "Big Data and Business Intelligence", + "Data Quality and Management" + ], + "discovered_via": [ + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2608268963", + "title": "It doesn\u2019t hurt to ask: Question-asking increases liking.", + "year": 2017, + "url": "https://doi.org/10.1037/pspi0000097", + "doi": "https://doi.org/10.1037/pspi0000097", + "abstract": "Conversation is a fundamental human experience that is necessary to pursue intrapersonal and interpersonal goals across myriad contexts, relationships, and modes of communication. In the current research, we isolate the role of an understudied conversational behavior: question-asking. Across 3 studies of live dyadic conversations, we identify a robust and consistent relationship between question-asking and liking: people who ask more questions, particularly follow-up questions, are better liked by their conversation partners. When people are instructed to ask more questions, they are perceived as higher in responsiveness, an interpersonal construct that captures listening, understanding, validation, and care. We measure responsiveness with an attitudinal measure from previous research as well as a novel behavioral measure: the number of follow-up questions one asks. In both cases, responsiveness explains the effect of question-asking on liking. In addition to analyzing live get-to-know-you conversations online, we also studied face-to-face speed-dating conversations. We trained a natural language processing algorithm as a \"follow-up question detector\" that we applied to our speed-dating data (and can be applied to any text data to more deeply understand question-asking dynamics). The follow-up question rate established by the algorithm showed that speed daters who ask more follow-up questions during their dates are more likely to elicit agreement for second dates from their partners, a behavioral indicator of liking. We also find that, despite the persistent and beneficial effects of asking questions, people do not anticipate that question-asking increases interpersonal liking. (PsycINFO Database Record", + "citation_count": 181, + "topics": [ + "Evolutionary Psychology and Human Behavior", + "Social and Intergroup Psychology", + "Media Influence and Health" + ], + "discovered_via": [ + "neighbor:openalex:W4407762039" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2042876500", + "title": "What do teachers teach in introductory programming?", + "year": 2006, + "url": "https://doi.org/10.1145/1151588.1151593", + "doi": "https://doi.org/10.1145/1151588.1151593", + "abstract": "In this article, we try to create a general, worldwide picture of teachers' opinion about what should be taught in introductory programming courses. We focus on the debate about restructuring CS1. The study explores what teachers believe is important to teach, what they actually teach, and what students find most difficult (according to their teachers). what is the general approach to teaching (programming language, IDE, object-orientation or not, type of institution), what topics are taught, and what role do the areas that novices find difficult play in introductory programming course.In addition, we explore how these specific topics fit into a larger conceptual classification: Earlier studies of topics taught in introductory programming focuses only on one dimension of a given topic - either relevance or difficulty. In this study, we evaluate each topic regarding three dimensions: relevance, difficulty and the cognitive level (according to Bloom's taxonomy). This allows giving a more faceted picture of teachers' beliefs in teaching introductory programming courses.Furthermore, we assess the role of findings from the eighties in today's teaching: The need to understand five different areas of programming. Are these areas still in focus; are they relevant, and what is the connection to the topics taught - especially object-oriented (OO) topics?A special focus is given on students' understanding of the execution of a (OO) program; one of the five areas. In order to connect the ideas of a notional machine to OO concepts we present a four levelled competence hierarchy for object-interaction.Teachers assessed the area 'understanding the notional machine' as least important. Despite this, they assessed the herachy of object-interaction - meant as basis for a notional machine for the OO-paradigm - as an important aspect.Although teachers stress the importance of teaching general abstract structures, teaching seems to focus on concrete programming issues. A conclusion for further research on teaching OO programming and concerning the hierarchy of object interaction is that teaching is not only a matter of topics, but also a matter of perspective on teaching the topics.", + "citation_count": 181, + "topics": [ + "Teaching and Learning Programming", + "Educational Games and Gamification", + "Genetics, Bioinformatics, and Biomedical Research" + ], + "discovered_via": [ + "neighbor:openalex:W4390315357" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1163009438", + "title": "The Delphi Method Research Strategy in Studies of Information Systems", + "year": 2015, + "url": "https://doi.org/10.17705/1cais.03702", + "doi": "https://doi.org/10.17705/1cais.03702", + "abstract": "In this paper, we discuss the nature and use of the Delphi methodology in information systems research. More specifically, we explore how and why it may be used. We discuss criteria for evaluating Delphi research and define characteristics useful for categorizing the studies. We review Delphi application use in IS research over the last 23 years, summarize lessons learned from prior studies, offer suggestions for improvement, and present guidelines for employing this distinctly useful qualitative method in future information systems research studies.", + "citation_count": 180, + "topics": [ + "Software Engineering Techniques and Practices", + "Delphi Technique in Research", + "Information Technology Governance and Strategy" + ], + "discovered_via": [ + "neighbor:openalex:W4391830676" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2966294766", + "title": "Gender diversity in boardrooms \u2013 A literature review", + "year": 2019, + "url": "https://doi.org/10.1080/23322039.2019.1644703", + "doi": "https://doi.org/10.1080/23322039.2019.1644703", + "abstract": "This paper examines the advancement of literature on gender diversity on corporate boards (board gender diversity). We discuss important management theories cited in the literature and examine the factors that affect board gender diversity. We present evidence from developed and emerging markets based on a review of studies to show how board gender diversity impacts a firm performance. We also review growing literature on the gender quota legislation that mandates the appointment of female director(s) on corporate boards. Research on board gender diversity reveals director characteristics, firm size, board size, board diversity, industry, type of ownership, customer base, and social and cultural characteristics as the factors that influence representation of female directors on corporate boards. Studies on the impact of board gender diversity on firm performance present inconclusive results. In a similar vein, studies on the impact of gender quota legislation on firm performance also present mixed results. Our study contributes to the growing literature on board gender diversity and provides a further understanding of factors that influence gender diversity on corporate boards. It also offers insights to regulators on potential limitations and the benefits of gender quota legislation.", + "citation_count": 178, + "topics": [ + "Gender Diversity and Inequality", + "Corporate Finance and Governance", + "Family Business Performance and Succession" + ], + "discovered_via": [ + "neighbor:openalex:W4322742241" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W73117955", + "title": "Alice: rapid prototyping for virtual reality", + "year": 1995, + "url": "https://doi.org/10.1109/38.376600", + "doi": "https://doi.org/10.1109/38.376600", + "abstract": "Virtual reality has sparked many people's imaginations, but writing VR programs remains difficult. Besides the obvious problems of managing arcane I/O devices (trackers, gloves, and so on), the programs must allow the participant to operate effectively in the immersive environment. Virtual environments present a new medium for both the participant and the programmer/author. In the University of Virginia's User Interface Group, we believe the best way to accelerate development in a new medium such as VR is to provide tools that allow people without highly technical backgrounds to create programs for it. These novice authors must be able to quickly try different nuances of an idea. They must be able to easily ask \"what if\" questions. To support this goal, we are developing Alice, a rapid prototyping environment that can generate VR environments. The name \"Alice\" honors Lewis Carroll's heroine, who explored a rapidly changing, dynamic environment.<>", + "citation_count": 173, + "topics": [ + "Interactive and Immersive Displays", + "Tactile and Sensory Interactions", + "Virtual Reality Applications and Impacts" + ], + "discovered_via": [ + "neighbor:openalex:W4390315357" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1966272739", + "title": "Effects of trust and perceived risk on user acceptance of a new technology service", + "year": 2013, + "url": "https://doi.org/10.2224/sbp.2013.41.4.587", + "doi": "https://doi.org/10.2224/sbp.2013.41.4.587", + "abstract": "We explored the precise impacts of trust and perceived risk on the core constructs of the unified theory of acceptance and use of technology (UTAUT). We applied the UTAUT model to the novel context of the Certified e-Document Authority (CeDA) service used in the Republic of Korea. Our results from structural equation modeling largely supported our hypotheses. Trust and perceived risk were shown to be direct antecedents of intention to use, and trust functioned as an indirect antecedent. Performance expectancy and social influence were shown to positively affect behavioral intention. Effort expectancy was also shown to influence performance expectancy. The combination of trust, perceived risk, performance expectancy, effort expectancy, and social influence accounted for 55.3% of the variance observed in users' intention to use CeDA services.", + "citation_count": 172, + "topics": [ + "Technology Adoption and User Behaviour", + "Digital Marketing and Social Media", + "Customer Service Quality and Loyalty" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1973095737", + "title": "The impact of situational factors on personnel decisions concerning women: Varying the sex composition of the applicant pool", + "year": 1980, + "url": "https://doi.org/10.1016/0030-5073(80)90074-4", + "doi": "https://doi.org/10.1016/0030-5073(80)90074-4", + "abstract": "", + "citation_count": 171, + "topics": [ + "Gender Diversity and Inequality", + "Social and Intergroup Psychology", + "Gender Roles and Identity Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4385952870" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1645738616", + "title": "Extracting symbolic rules from trained neural network ensembles", + "year": 2003, + "url": "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.16.2078", + "doi": null, + "abstract": "Neural network ensemble can significantly improve the generalization ability of neural network based systems. However, its comprehensibility is even worse than that of a single neural network because it comprises a collection of individual neural networks. In this paper, an approach named REFNE is proposed to improve the comprehensibility of trained neural network ensembles that perform classification tasks. REFNE utilizes the trained ensembles to generate instances and then extracts symbolic rules from those instances. It gracefully breaks the ties made by individual neural networks in prediction. It also employs specific discretization scheme, rule form, and fidelity evaluation mechanism. Experiments show that with different configurations, REFNE can extract rules with good fidelity that well explain the function of trained neural network ensembles, or rules with strong generalization ability that are even better than the trained neural network ensembles in prediction.", + "citation_count": 169, + "topics": [ + "Neural Networks and Applications", + "Fuzzy Logic and Control Systems", + "Rough Sets and Fuzzy Logic" + ], + "discovered_via": [ + "neighbor:openalex:W4386142022" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1515345476", + "title": "Normativity, Ethics, and the UN Guiding Principles on Business and Human Rights: A Critical Assessment", + "year": 2015, + "url": "https://doi.org/10.1080/14754835.2015.1005733", + "doi": "https://doi.org/10.1080/14754835.2015.1005733", + "abstract": "This article critically assesses the work of the UN Special Representative for Business and Human Rights (SRSG) John Ruggie. The article adopts a normative perspective on the issue. Thus, its critique is derived from the standpoint of ethics. The SRSG was instrumental in shifting the burden of proof to those who deny corporate human rights responsibilities. This achievement, however, is relativized by the very restrictive interpretation of such responsibilities, both in terms of their scope as well as the normative force assigned to them. Finally, the article explores and analyzes the SRSG's relative reluctance to address and engage with ethical categories more explicitly. It outlines the dangers and blind spots that may result from this reluctance and reflects on the role that ethics can, and perhaps should, play in the broader debate on business and human rights.", + "citation_count": 165, + "topics": [ + "Corporate Law and Human Rights", + "International Law and Human Rights", + "Human Rights and Development" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3124327509", + "title": "Measuring the Importance of Precursor Steps to Transformative Learning", + "year": 2009, + "url": "https://doi.org/10.1177/0741713609333084", + "doi": "https://doi.org/10.1177/0741713609333084", + "abstract": "Transformative learning has been important in the development of adult education since Jack Mezirow proposed it more than 35 years ago as a theoretical description of the steps learners undergo in changing their worldviews. However, despite much qualitative research, little quantitative study has been made of the incidence of transformative learning or the 10 steps predicted by Mezirow to precede it. This study of 256 undergraduate business school students reports the incidence of transformative learning and each of the 10 precursor steps. The more steps respondents remembered experiencing, the more they also reported transformative learning. The highest incidence of reporting transformative learning was associated with the precursor step of critical reflection, followed by the steps of disorienting dilemmas and trying on new roles. Implications for practitioners and researchers are discussed.", + "citation_count": 157, + "topics": [ + "Adult and Continuing Education Topics", + "Ego Development and Educational Practices", + "Critical and Liberation Pedagogy" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2992233710", + "title": "Discrimination, artificial intelligence, and algorithmic decision-making", + "year": 2025, + "url": "https://doi.org/10.48550/arxiv.2510.13465", + "doi": "https://doi.org/10.48550/arxiv.2510.13465", + "abstract": "Artificial intelligence (AI) has a huge impact on our personal lives and also on our democratic society as a whole. While AI offers vast opportunities for the benefit of people, its potential to embed and perpetuate bias and discrimination remains one of the most pressing challenges deriving from its increasing use. This new study, which was prepared by Prof. Frederik Zuiderveen Borgesius for the Anti-discrimination Department of the Council of Europe, elaborates on the risks of discrimination caused by algorithmic decision-making and other types of artificial intelligence (AI).", + "citation_count": 156, + "topics": [ + "Impact of AI and Big Data on Business and Society" + ], + "discovered_via": [ + "query:algorithmic hiring employment discrimination automated decision systems law regulation audit privacy" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W61620987", + "title": "The Cambridge Companion to Utilitarianism", + "year": 2014, + "url": "https://doi.org/10.1017/cco9781139096737", + "doi": "https://doi.org/10.1017/cco9781139096737", + "abstract": "Utilitarianism, the approach to ethics based on the maximization of overall well-being, continues to have great traction in moral philosophy and political thought. This Companion offers a systematic exploration of its history, themes, and applications. First, it traces the origins and development of utilitarianism via the work of Jeremy Bentham, John Stuart Mill, Henry Sidgwick, and others. The volume then explores issues in the formulation of utilitarianism, including act versus rule utilitarianism, actual versus expected consequences, and objective versus subjective theories of well-being. Next, utilitarianism is positioned in relation to Kantianism and virtue ethics, and the possibility of conflict between utilitarianism and fairness is considered. Finally, the volume explores the modern relevance of utilitarianism by considering its practical implications for contemporary controversies such as military conflict and global warming. The volume will be an important resource for all those studying moral philosophy, political philosophy, political theory, and history of ideas.", + "citation_count": 156, + "topics": [ + "Philosophical Ethics and Theory", + "Political Philosophy and Ethics" + ], + "discovered_via": [ + "neighbor:openalex:W4394590365" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2905011444", + "title": "A data mining-based framework for supply chain risk management", + "year": 2018, + "url": "https://doi.org/10.1016/j.cie.2018.12.017", + "doi": "https://doi.org/10.1016/j.cie.2018.12.017", + "abstract": "", + "citation_count": 154, + "topics": [ + "Supply Chain Resilience and Risk Management", + "Quality and Supply Management", + "Occupational Health and Safety Research" + ], + "discovered_via": [ + "neighbor:openalex:W4404254054" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2046159940", + "title": "The relative predictive validity of two therapeutic alliance measures in cognitive therapy.", + "year": 1991, + "url": "https://doi.org/10.1037/1040-3590.3.2.188", + "doi": "https://doi.org/10.1037/1040-3590.3.2.188", + "abstract": "The current study investigated the relative predictive validity of 2 self-report measures of the therapeutic alliance for patients receiving a version of short-term cognitive therapy that places particular emphasis on the importance of the therapeutic relationship. The Working Alliance Inventory (WAI) and the California Psychotherapy Alliance Scale (CALPAS) were administered at the third session to a sample of 22 patients receiving short-term cognitive therapy, and their relationship with outcome on a variety of measures was calculated", + "citation_count": 154, + "topics": [ + "Psychotherapy Techniques and Applications", + "Counseling, Therapy, and Family Dynamics", + "Attachment and Relationship Dynamics" + ], + "discovered_via": [ + "neighbor:openalex:W4409825334" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W291854156", + "title": "The Expressive Powers of Law: Theories and Limits", + "year": 2017, + "url": "https://doi.org/10.4159/harvard.9780674735965", + "doi": "https://doi.org/10.4159/harvard.9780674735965", + "abstract": "When asked why people obey the law, legal scholars usually give two answers. deters illicit activities by specifying sanctions, and it possesses legitimate authority in the eyes of society. Richard McAdams shifts the prism on this familiar question to offer another compelling explanation of how the law creates compliance: through its expressive power to coordinate our behavior and inform our beliefs.People seek order, and they sometimes obtain a mutually shared benefit when each expects the other to behave in accordance with law. Traffic regulations, for example, coordinate behavior by expressing an orderly means of driving. A traffic sign that tells one driver to yield to another creates expectations in the minds of both drivers and so allows each to avoid collision. McAdams generalizes from traffic to constitutional and international law and many other domains. In addition to its coordinating function, law expresses information. Legislation reveals something important about the risks of the behavior being regulated, and social attitudes toward it. Anti-smoking laws, for example, signal both the lawmakers recognition of the health risks associated with smoking and the public s general disapproval. This information causes individuals to update their beliefs and alter their behavior.McAdams shows how an expressive theory explains the law s sometimes puzzling efficacy, as when tribunals are able to resolve disputes even though they lack coercive power or legitimacy. The Expressive Powers of Law contributes to our understanding of the mechanisms by which law simply by what it says rather than what it sanctions generates compliance.", + "citation_count": 149, + "topics": [ + "Law, Rights, and Freedoms", + "Legal Systems and Judicial Processes", + "Legal and Constitutional Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4394590365" + ], + "relevance_score": 0.0, + "relevance_reason": "General legal theory about law's expressive effects with no AI hiring connection.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W842190952", + "title": "Machines without Principals: Liability Rules and Artificial Intelligence", + "year": 2014, + "url": "http://digital.law.washington.edu/dspace-law/bitstream/handle/1773.1/1322/89WLR0117.pdf?sequence=1", + "doi": null, + "abstract": "INTRODUCTIONThe idea that humans could, at some point, develop machines that actually think for themselves and act autonomously has been embedded in our literature and culture since the beginning of civilization.1 But these ideas were generally thought to be religious expressions-what one scholar describes as an effort to forge our own Gods2-or pure science fiction. There was one important thread that tied together these visions of a special breed of superhuman men/machines: They invariably were stronger, smarter, and sharper analytically; that is, superior in all respects to humans, except for those traits involving emotional intelligence and empathy. But science fiction writers were of two minds about the capacity of super-smart machines to make life better for humans.One vision was uncritically Utopian. Intelligent machines, this account goes, would transform and enlighten society by performing the mundane, mind-numbing work that keeps humans from pursuing higher intellectual, spiritual, and artistic callings.3 This view was captured in the popular animated 1960s television show The Jetsons.4 As its title suggests, the show's vision is decidedly futuristic. The main character, George Jetson, lives with his family in a roomy, bright, and lavishly furnished apartment that seems to float in the sky. George and his family travel in a flying saucer-like car that drives itself and folds into a small briefcase. All of the family's domestic needs are taken care of by Rosie, the robotic family maid and housekeeper, who does the household chores and much of the parenting.5 George does work. He is employed as a digital index operator by Spacely's Space Sprockets, which makes high tech equipment. George often complains of overwork, even though he appears to simply push buttons on a computer for three hours a day, three days a week.6 In other words, the Jetsons live the American dream of the future.In tangible ways, this Utopian vision of the partnership between humans and highly intelligent machines is being realized. Today, supercomputers can beat humans at their own games. IBM's Deep Blue can beat the pants off chess grand-masters, while its sister-super- computer Watson can clobber the reigning Jeopardy champions.7 But intelligent machines are more than show. Highly sophisticated robots and other intelligent machines perform critical functions that not long ago were thought to be within the exclusive province of humans. They pilot sophisticated aircraft; perform delicate surgery; study the landscape of Mars; and through smart nanotechnology, microscopic machines may soon deliver targeted medicines to areas within the body that are otherwise unreachable.8 In every one of these examples, machines perform these complex and at times dangerous tasks as well as, if not better than, humans.But science fiction writers also laid out a darker vision of intelligent machines and feared that, at some point, autonomously thinking machines would turn on humans. Some of the best science fiction expresses this dystopian view, including Stanley Kubrick's 1968 classic film 2001: A Space Odyssey.9 The film's star is not the main character, (Dr. David Bowman, played by Keir Dullea), or (Dr. Frank Poole, played by Gary Lockwood), who are astronauts on a secret and mysterious mission to Jupiter. Instead, the character who rivets our attention is HAL 9000,10 the all-knowing supercomputer who controls most of the ship's operations, but does so under the nominal command of the astronauts. The complexity of the relationship between man and the super-intelligent machine is revealed early in the film. During a pre- mission interview, HAL claims that he is foolproof and incapable of error,11 displaying human-like hubris. And when Dave is asked if HAL has genuine emotions, he replies that HAL appears to, but that the truth is unknown.12Once the mission begins, tensions between HAL and the astronauts start to surface. \u2026", + "citation_count": 149, + "topics": [ + "Legal Systems and Judicial Processes" + ], + "discovered_via": [ + "neighbor:openalex:W4400303336" + ], + "relevance_score": 0.0, + "relevance_reason": "General discussion of AI liability and autonomous machines without a hiring-specific application.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W565326003", + "title": "Managing human resources : personnel management in transition", + "year": 2005, + "url": "http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015204080&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA", + "doi": null, + "abstract": "List of Contributors. List of Figures. List of Tables. List of Boxes. Preface. Part I: Managing Human Resources in Context. 1. Personnel Management in Transition: Stephen Bach (King's College, University of London). 2. Personnel Management and European Integration: A Case of Indelible Imprint? Keith Sisson (University of Warwick Business School). 3. Managing Human Resources in Multinational Companies: Tony Edwards (King's College, University of London) and Anthony Ferner (De Montfort University). 4. Managing Human Resources in the Networked Organization: Trevor Colling (De Montfort University). Part II: Employee Resourcing. 5. Recruitment and Selection: Sue Newell (Bentley College). 6. Work-Life Balance: Challenging the Overwork Culture: Janet Walsh (King's College, University of London). 7. Walking the Talk? Equality and Diversity in Employment: Linda Dickens (University of Warwick Business School). Part III: Employee Development. 8. Skills, Training and the Quest for the Holy Grail of Influence and Status: Ewart Keep (University of Warwick Business School). 9. Management Development and Career Management: David Guest (King's College, University of London)and Zella King (University of Reading). 10. Employer and Employee Branding: HR or PR? Martin Edwards (King's College, University of London). Part IV: Pay and Performance. 11. New Directions in Performance Management: Stephen Bach (King's College, University of London). 12. Remuneration Systems: Ian Kessler (Templeton College, University of Oxford). 13. Customer Service Work, Emotional Labour and Performance: Stephen Deery (King's College, University of London). Part V: Work Relations. 14. Discipline and Attendance: A Murky Aspect of People Management: Paul Edwards (University of Warwick Business School). 15. Direct Participation and Involvement: Mick Marchington (University of Manchester Business School) and Adrian Wilkinson (University of Loughborough Business School). 16. Management and Trade Unions: Partnership at Work? Stephanie Tailby (University of the West of England) and David Winchester (University of Warwick Business School). Index", + "citation_count": 148, + "topics": [ + "Human Resource and Talent Management" + ], + "discovered_via": [ + "neighbor:openalex:W4386714740" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1516398821", + "title": "OECD Digital Economy Papers", + "year": 2010, + "url": "https://doi.org/10.1787/20716826", + "doi": "https://doi.org/10.1787/20716826", + "abstract": "This report aims to review research on data localisation as an emerging impediment to data flows and data privacy protection. The report serves to inform the review of the implementation of the OECD Privacy Guidelines [OECD/LEGAL/0188] and guide further discussions amongst members of the OECD Working Party on Data Governance and Privacy in the Digital Economy (DGP), the OECD Secretariat and the Privacy Guidelines Expert Group that was formed to support the review.
This paper was drafted by Professor Dan Jerker B. Svantesson, Professor of Law, Bond University with feedback from Professor Christopher Kuner, Founder and Co-Director, Brussels Privacy Research Hub, Vrije, Universiteit Brussel , Elettra Ronchi and Lauren Bourke of the OECD Secretariat. It benefitted from the input of the expert group established to support the review of the OECD Privacy Guidelines and delegates of the Working Party on Data Governance and Privacy. The paper was further discussed at the virtual OECD Expert Roundtable on \u201cData localisation and Trusted Government Access to Data\u201d held on 5-6 October 2020. The work was made possible by the generous contributions of Japan.

This paper should not be reported as representing the official views of the OECD or of its member
countries. The opinions expressed and arguments employed are those of the author. It describes
preliminary results or research in progress by the author and is published to stimulate discussion on a broad range of issues on which the OECD works. Comments on this paper are welcomed, and may be sent to the Directorate for Science, Technology and Innovation, OECD, 2 rue Andr\u00e9-Pascal, 75775 Paris
Cedex 16, France.
N", + "citation_count": 143, + "topics": [ + "Corporate Taxation and Avoidance", + "Economic Growth and Productivity" + ], + "discovered_via": [ + "neighbor:openalex:W4318407310" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2076536433", + "title": "Building a Trustworthy State in Post-Socialist Transition", + "year": 2004, + "url": "https://doi.org/10.1057/9781403981103", + "doi": "https://doi.org/10.1057/9781403981103", + "abstract": "Building a Trustworthy State in Post-Socialist Transition considers the problems and prospects for creating trustworthy and reliable public institutions in the aftermath of the transition from sociali", + "citation_count": 143, + "topics": [ + "Eastern European Communism and Reforms", + "Russia and Soviet political economy" + ], + "discovered_via": [ + "neighbor:openalex:W4381848566" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1506598730", + "title": "The Oxford Handbook of Diversity and Work", + "year": 2013, + "url": "https://doi.org/10.1093/oxfordhb/9780199736355.001.0001", + "doi": "https://doi.org/10.1093/oxfordhb/9780199736355.001.0001", + "abstract": "Abstract To keep pace with the changing business environment as globalization permeates both consumer and labor markets, this handbook offers the most current research in the workplace diversity, exploring what diversity means and its impact on group and organizational functioning. The volume is comprised of eight sections. The first section provides a fundamental introduction and overview to the history and current state of workplace diversity. The second section explores various conceptualizations of diversity. The third section focuses on psychological perspectives on diversity, touching on the self in diverse work contexts, intergroup bias, and the experience of stigma. The fourth section deals with interactionist perspectives on diversity, including chapters on diversity as knowledge exchange, diversity as disagreement, and diversity as network connections. The fifth section provides contextual perspectives on diversity, e.g., how context shapes diversity outcomes, diversity cognition and climate, and viewing diversity as a competitive advantage. Sections six and seven consider practice perspectives and systems perspectives in diversity, touching on leadership, diversity training, work-life interface, and law and diversity. Finally, the book concludes with a chapter on future directions for diversity theory and research.", + "citation_count": 136, + "topics": [ + "Gender Diversity and Inequality" + ], + "discovered_via": [ + "neighbor:openalex:W4394590365" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1978162256", + "title": "Trained to accept?", + "year": 2010, + "url": "https://doi.org/10.1145/1753326.1753689", + "doi": "https://doi.org/10.1145/1753326.1753689", + "abstract": "A typical consent dialog was shown in 2 x 2 x 3 experimental variations to 80,000 users of an online privacy tool. We find that polite requests and button texts pointing to a voluntary decision decrease the probability of consent---in contrast to findings in social psychology. Our data suggests that subtle positive effects of polite requests indeed exist, but stronger negative effects of heuristic processing dominate the aggregated results. Participants seem to be habituated to coercive interception dialogs---presumably due to ubiquitous EULAs---and blindly accept terms the more their presentation resembles a EULA. Response latency and consultation of online help were taken as indicators to distinguish more systematic from heuristic responses.", + "citation_count": 135, + "topics": [ + "Deception detection and forensic psychology", + "Social and Intergroup Psychology", + "Psychology of Social Influence" + ], + "discovered_via": [ + "neighbor:openalex:W4392182430" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2726467123", + "title": "Is stock return predictability time-varying?", + "year": 2017, + "url": "https://doi.org/10.1016/j.intfin.2017.06.001", + "doi": "https://doi.org/10.1016/j.intfin.2017.06.001", + "abstract": "", + "citation_count": 134, + "topics": [ + "Financial Markets and Investment Strategies", + "Stock Market Forecasting Methods", + "Market Dynamics and Volatility" + ], + "discovered_via": [ + "neighbor:openalex:W4386142022" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2059189632", + "title": "Domain driven data mining in human resource management: A review of current research", + "year": 2012, + "url": "https://doi.org/10.1016/j.eswa.2012.10.059", + "doi": "https://doi.org/10.1016/j.eswa.2012.10.059", + "abstract": "", + "citation_count": 134, + "topics": [ + "Data Mining Algorithms and Applications", + "AI and HR Technologies", + "Imbalanced Data Classification Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1999907667", + "title": "Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features", + "year": 2011, + "url": "https://doi.org/10.1186/1471-2105-12-412", + "doi": "https://doi.org/10.1186/1471-2105-12-412", + "abstract": "BACKGROUND: Machine learning methods are nowadays used for many biological prediction problems involving drugs, ligands or polypeptide segments of a protein. In order to build a prediction model a so called training data set of molecules with measured target properties is needed. For many such problems the size of the training data set is limited as measurements have to be performed in a wet lab. Furthermore, the considered problems are often complex, such that it is not clear which molecular descriptors (features) may be suitable to establish a strong correlation with the target property. In many applications all available descriptors are used. This can lead to difficult machine learning problems, when thousands of descriptors are considered and only few (e.g. below hundred) molecules are available for training. RESULTS: The CoEPrA contest provides four data sets, which are typical for biological regression problems (few molecules in the training data set and thousands of descriptors). We applied the same two-step training procedure for all four regression tasks. In the first stage, we used optimized L1 regularization to select the most relevant features. Thus, the initial set of more than 6,000 features was reduced to about 50. In the second stage, we used only the selected features from the preceding stage applying a milder L2 regularization, which generally yielded further improvement of prediction performance. Our linear model employed a soft loss function which minimizes the influence of outliers. CONCLUSIONS: The proposed two-step method showed good results on all four CoEPrA regression tasks. Thus, it may be useful for many other biological prediction problems where for training only a small number of molecules are available, which are described by thousands of descriptors.", + "citation_count": 134, + "topics": [ + "Computational Drug Discovery Methods", + "Machine Learning in Bioinformatics", + "Machine Learning in Materials Science" + ], + "discovered_via": [ + "neighbor:openalex:W4386142022" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2612632117", + "title": "An Analysis of Glass Ceiling Perceptions in the Accounting Profession", + "year": 2018, + "url": "https://doi.org/10.1007/s10551-018-4054-4", + "doi": "https://doi.org/10.1007/s10551-018-4054-4", + "abstract": "", + "citation_count": 132, + "topics": [ + "Gender Diversity and Inequality", + "Accounting Education and Careers", + "Auditing, Earnings Management, Governance" + ], + "discovered_via": [ + "neighbor:openalex:W4391598857" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4303857474", + "title": "A novel method for improving the robustness of deep learning-based malware detectors against adversarial attacks", + "year": 2022, + "url": "https://doi.org/10.1016/j.engappai.2022.105461", + "doi": "https://doi.org/10.1016/j.engappai.2022.105461", + "abstract": "", + "citation_count": 130, + "topics": [ + "Advanced Malware Detection Techniques", + "Adversarial Robustness in Machine Learning", + "Network Security and Intrusion Detection" + ], + "discovered_via": [ + "neighbor:openalex:W4318407310" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W16967605", + "title": "The Search for Uniqueness and Valuation of Scarcity", + "year": 1980, + "url": "https://doi.org/10.1007/978-1-4613-3087-5_3", + "doi": "https://doi.org/10.1007/978-1-4613-3087-5_3", + "abstract": "", + "citation_count": 130, + "topics": [ + "Opinion Dynamics and Social Influence", + "Social Media and Politics" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2956449106", + "title": "Resilient supplier selection in logistics 4.0 with heterogeneous information", + "year": 2019, + "url": "https://doi.org/10.1016/j.eswa.2019.07.016", + "doi": "https://doi.org/10.1016/j.eswa.2019.07.016", + "abstract": "", + "citation_count": 129, + "topics": [ + "Multi-Criteria Decision Making", + "Sustainable Supply Chain Management", + "Risk and Safety Analysis" + ], + "discovered_via": [ + "neighbor:openalex:W4404254054" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1833550036", + "title": "Automated prediction and analysis of job interview performance: The role of what you say and how you say it", + "year": 2015, + "url": "https://doi.org/10.1109/fg.2015.7163127", + "doi": "https://doi.org/10.1109/fg.2015.7163127", + "abstract": "Ever wondered why you have been rejected from a job despite being a qualified candidate? What went wrong? In this paper, we provide a computational framework to quantify human behavior in the context of job interviews. We build a model by analyzing 138 recorded interview videos (total duration of 10.5 hours) of 69 internship-seeking students from Massachusetts Institute of Technology (MIT) as they spoke with professional career counselors. Our automated analysis includes facial expressions (e.g., smiles, head gestures), language (e.g., word counts, topic modeling), and prosodic information (e.g., pitch, intonation, pauses) of the interviewees. We derive the ground truth labels by averaging over the ratings of 9 independent judges. Our framework automatically predicts the ratings for interview traits such as excitement, friendliness, and engagement with correlation coefficients of 0.73 or higher, and quantifies the relative importance of prosody, language, and facial expressions. According to our framework, it is recommended to speak more fluently, use less filler words, speak as \u201cwe\u201d (vs. \u201cI\u201d), use more unique words, and smile more.", + "citation_count": 126, + "topics": [ + "Topic Modeling", + "Sentiment Analysis and Opinion Mining", + "Mental Health via Writing" + ], + "discovered_via": [ + "neighbor:openalex:W4387789492" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2765808856", + "title": "The Humanizing Voice: Speech Reveals, and Text Conceals, a More Thoughtful Mind in the Midst of Disagreement", + "year": 2017, + "url": "https://doi.org/10.1177/0956797617713798", + "doi": "https://doi.org/10.1177/0956797617713798", + "abstract": "A person's speech communicates his or her thoughts and feelings. We predicted that beyond conveying the contents of a person's mind, a person's speech also conveys mental capacity, such that hearing a person explain his or her beliefs makes the person seem more mentally capable-and therefore seem to possess more uniquely human mental traits-than reading the same content. We expected this effect to emerge when people are perceived as relatively mindless, such as when they disagree with the evaluator's own beliefs. Three experiments involving polarizing attitudinal issues and political opinions supported these hypotheses. A fourth experiment identified paralinguistic cues in the human voice that convey basic mental capacities. These results suggest that the medium through which people communicate may systematically influence the impressions they form of each other. The tendency to denigrate the minds of the opposition may be tempered by giving them, quite literally, a voice.", + "citation_count": 125, + "topics": [ + "Social and Intergroup Psychology", + "Psychology of Moral and Emotional Judgment", + "Cultural Differences and Values" + ], + "discovered_via": [ + "neighbor:openalex:W4407762039" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2054921494", + "title": "Structuring Flipped Classes with Lightweight Teams and Gamification", + "year": 2015, + "url": "https://doi.org/10.1145/2676723.2677240", + "doi": "https://doi.org/10.1145/2676723.2677240", + "abstract": "We present a new approach to help make computer science classes both more social and more effective: \"lightweight teams\". Lightweight teams are class teams in which the team members have little or no direct impact on each other's final grades, yet where there is a significant component of peer teaching, peer learning and long-term socialization built into the curriculum. We explain how lightweight teams have been used in a CS1 class at our institution, and how this approach, combined with a flipped class approach and gamification, has led to high levels of student engagement, despite the difficulty of the material and the frustration that is common to those first learning to program.", + "citation_count": 125, + "topics": [ + "Teaching and Learning Programming", + "Educational Games and Gamification", + "Innovative Teaching Methods" + ], + "discovered_via": [ + "neighbor:openalex:W4390315357" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W106141397", + "title": "Managing Human Assets: The Groundbreaking Harvard Business School Program", + "year": 1984, + "url": "https://www.hbs.edu/faculty/Pages/item.aspx?num=41269", + "doi": null, + "abstract": "", + "citation_count": 124, + "topics": [ + "Defense, Military, and Policy Studies", + "Public Procurement and Policy", + "Leadership, Behavior, and Decision-Making Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4383913712" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3016229568", + "title": "Trustworthy artificial intelligence (AI) in education", + "year": 2020, + "url": "https://doi.org/10.1787/a6c90fa9-en", + "doi": "https://doi.org/10.1787/a6c90fa9-en", + "abstract": "This paper was written to support the G20 artificial intelligence (AI) dialogue. With the rise of artificial intelligence (AI), education faces two challenges: reaping the benefits of AI to improve education processes, both in the classroom and at the system level; and preparing students for new skillsets for increasingly automated economies and societies. AI applications are often still nascent, but there are many examples of promising uses that foreshadow how AI might transform education. With regard to the classroom, this paper highlights how AI can accelerate personalised learning, the support of students with special needs. At the system level, promising uses include predictive analysis to reduce dropout, and assessing new skillsets. A new demand for complex skills that are less easy to automate (e.g. higher cognitive skills like creativity and critical thinking) is also the consequence of AI and digitalisation. Reaching the full potential of AI requires that stakeholders trust not only the technology, but also its use by humans. This raises new policy challenges around \u201ctrustworthy AI\u201d, encompassing the privacy and security of data, but also possible wrongful uses of data leading to biases against individuals or groups.", + "citation_count": 123, + "topics": [ + "Online Learning and Analytics", + "Explainable Artificial Intelligence (XAI)", + "Artificial Intelligence in Healthcare and Education" + ], + "discovered_via": [ + "neighbor:openalex:W2992233710" + ], + "relevance_score": 0.0, + "relevance_reason": "Focuses on trustworthy AI in education, not hiring or employment decisions.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W3009306974", + "title": "Toward fairness in AI for people with disabilities SBG@a research roadmap", + "year": 2020, + "url": "https://doi.org/10.1145/3386296.3386298", + "doi": "https://doi.org/10.1145/3386296.3386298", + "abstract": "AI technologies have the potential to dramatically impact the lives of people with disabilities (PWD). Indeed, improving the lives of PWD is a motivator for many state-of-the-art AI systems, such as automated speech recognition tools that can caption videos for people who are deaf and hard of hearing, or language prediction algorithms that can augment communication for people with speech or cognitive disabilities. However, widely deployed AI systems may not work properly for PWD, or worse, may actively discriminate against them. These considerations regarding fairness in AI for PWD have thus far received little attention. In this position paper, we identify potential areas of concern regarding how several AI technology categories may impact particular disability constituencies if care is not taken in their design, development, and testing. We intend for this risk assessment of how various classes of AI might interact with various classes of disability to provide a roadmap for future research that is needed to gather data, test these hypotheses, and build more inclusive algorithms.", + "citation_count": 123, + "topics": [ + "Artificial Intelligence in Healthcare and Education", + "Ethics and Social Impacts of AI", + "Mobile Crowdsensing and Crowdsourcing" + ], + "discovered_via": [ + "neighbor:openalex:W4404228668" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2919896475", + "title": "Unconscious bias and the medical model: How the social model may hold the key to transformative thinking about disability discrimination", + "year": 2019, + "url": "https://doi.org/10.1177/1358229118820742", + "doi": "https://doi.org/10.1177/1358229118820742", + "abstract": "This article seeks to gain access to a new way to engage with disability discrimination and the legal approaches to it by focusing on the two central models: the medical and social models. It discusses how the law has based the definition of disability on the medical model and suggests that this may strengthen some of the underlying factors that contribute to segregation and discrimination of disabled people. This article argues that the law should now switch focus to the social model, in an attempt to transform people\u2019s attitudes towards disabled people and become a positive force to reduce discrimination. It makes reference to the reasonable adjustment duty contained in sections 20 and 21 Equality Act 2010, the Framework Directive and by way of comparison the American with Disabilities Act 1990. Relevant critical theories are integrated as a means to explore the conception and the hierarchy that exist between able-bodied individuals and disabled individuals.", + "citation_count": 123, + "topics": [ + "Disability Rights and Representation", + "Discrimination and Equality Law", + "Healthcare innovation and challenges" + ], + "discovered_via": [ + "neighbor:openalex:W4391598857" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2899883278", + "title": "When your resume is (not) turning you down: Modelling ethnic bias in resume screening", + "year": 2018, + "url": "https://doi.org/10.1111/1748-8583.12217", + "doi": "https://doi.org/10.1111/1748-8583.12217", + "abstract": "Abstract Resume screening is the first hurdle applicants typically face when they apply for a job. Despite the many empirical studies showing bias at the resume\u2010screening stage, fairness at this funnelling stage has not been reviewed systematically. In this paper, a three\u2010stage model of biased resume screening is presented. We first discuss relevant theoretical perspectives (e.g., job market signalling and impression formation theories) to explain why resume screening is vulnerable to biased decision\u2010making and ethnic discrimination in particular. On the basis of the best available evidence, we consider contingencies of ethnic discrimination in the applicant, the decision\u2010maker, and the broader context (e.g., organisation), as well as the effectiveness of interventions that might counter ethnic bias in resume screening. The paper ends with a critical agenda for further research and practice.", + "citation_count": 121, + "topics": [ + "Names, Identity, and Discrimination Research", + "Employer Branding and e-HRM", + "Gender Diversity and Inequality" + ], + "discovered_via": [ + "neighbor:openalex:W4381684347" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2911122801", + "title": "Dissecting Racial Bias in an Algorithm that Guides Health Decisions for 70 Million People", + "year": 2019, + "url": "https://doi.org/10.1145/3287560.3287593", + "doi": "https://doi.org/10.1145/3287560.3287593", + "abstract": "A single algorithm drives an important health care decision for over 70 million people in the US. When health systems anticipate that a patient will have especially complex and intensive future health care needs, she is enrolled in a 'care management' program, which provides considerable additional resources: greater attention from trained providers and help with coordination of her care.", + "citation_count": 119, + "topics": [ + "Healthcare Policy and Management" + ], + "discovered_via": [ + "neighbor:openalex:W4391598857" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4392172846", + "title": "Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond", + "year": 2024, + "url": "https://doi.org/10.1109/access.2024.3369912", + "doi": "https://doi.org/10.1109/access.2024.3369912", + "abstract": "In the past decade, the deployment of deep learning (Artificial Intelligence (AI)) methods has become pervasive across a spectrum of real-world applications, often in safety-critical contexts. This comprehensive research article rigorously investigates the ethical dimensions intricately linked to the rapid evolution of AI technologies, with a particular focus on the healthcare domain. Delving deeply, it explores a multitude of facets including transparency, adept data management, human oversight, educational imperatives, and international collaboration within the realm of AI advancement. Central to this article is the proposition of a conscientious AI framework, meticulously crafted to accentuate values of transparency, equity, answerability, and a human-centric orientation. The second contribution of the article is the in-depth and thorough discussion of the limitations inherent to AI systems. It astutely identifies potential biases and the intricate challenges of navigating multifaceted contexts. Lastly, the article unequivocally accentuates the pressing need for globally standardized AI ethics principles and frameworks. Simultaneously, it aptly illustrates the adaptability of the ethical framework proposed herein, positioned skillfully to surmount emergent challenges.", + "citation_count": 117, + "topics": [ + "Artificial Intelligence in Healthcare and Education", + "Explainable Artificial Intelligence (XAI)", + "Machine Learning in Healthcare" + ], + "discovered_via": [ + "query:AI hiring methods causal evaluation field experiment longitudinal quality of hire" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2064691740", + "title": "Disparities in Physicians' Interpretations of Heart Disease Symptoms by Patient Gender: Results of a Video Vignette Factorial Experiment", + "year": 2009, + "url": "https://doi.org/10.1089/jwh.2008.1007", + "doi": "https://doi.org/10.1089/jwh.2008.1007", + "abstract": "BACKGROUND: Previous studies have documented the underdiagnosis of coronary heart disease (CHD) in women, but less is known about which alternate diagnoses take precedence and whether additional patient factors modify possible gender bias. OBJECTIVE: To measure gender variation in clinical decision making, including (1) the number, types, and certainty levels of diagnoses considered and (2) how diagnoses vary according to patient characteristics, when patients have identical symptoms of CHD. METHODS: This was a factorial experiment presenting videotaped CHD symptoms, systematically altering patient gender, age, socioeconomic status (SES) and race, and physician gender and level of experience. The primary end point was physicians' most certain diagnosis. RESULTS: Physicians (n = 128) mentioned five diagnoses on average, most commonly heart, gastrointestinal, and mental health conditions. Physicians were significantly less certain of the underlying cause of symptoms among female patients regardless of age (p = 0.006), but only among middle-aged women were they significantly less certain of the CHD diagnosis (p < 0.001). Among middle-aged women, 31.3% received a mental health condition as the most certain diagnosis, compared with 15.6% of their male counterparts (p = 0.03). An interaction effect showed that females with high SES were most likely to receive a mental health diagnosis as the most certain (p = 0.006). CONCLUSIONS: Middle-aged female patients were diagnosed with the least confidence, whether for CHD or non-CHD conditions, indicating that their gender and age combination misled physicians, particularly toward mental health alternative diagnoses. Physicians should be aware of the potential for psychological symptoms to erroneously take a central role in the diagnosis of younger women.", + "citation_count": 112, + "topics": [ + "Cardiac Health and Mental Health", + "Acute Myocardial Infarction Research", + "Psychosomatic Disorders and Their Treatments" + ], + "discovered_via": [ + "neighbor:openalex:W4392172846" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W189308459", + "title": "Comparing Structured Interview Question Types: Construct Validity and Applicant Reactions", + "year": 1999, + "url": "https://doi.org/10.1023/a:1022914803347", + "doi": "https://doi.org/10.1023/a:1022914803347", + "abstract": "", + "citation_count": 112, + "topics": [ + "Employer Branding and e-HRM", + "Conflict Management and Negotiation", + "Communication in Education and Healthcare" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1980871615", + "title": "The recurring rainfall problem", + "year": 2014, + "url": "https://doi.org/10.1145/2632320.2632346", + "doi": "https://doi.org/10.1145/2632320.2632346", + "abstract": "Many studies have used Soloway's Rainfall problem to explore plan composition and programming errors by novice programmers. Few of these have explored students from CS1 courses that use functional programming. The concepts and programming styles commonly taught in such courses give CS1 students more viable plan-composition options than in traditional imperative CS1 courses. Using data from five functional-language CS1 courses at four schools, we show that our students choose different high-level structures and make fewer low-level errors compared to results of other Rainfall studies. We discuss the potential role of language in these results and raise various questions that could further explore these effects.", + "citation_count": 111, + "topics": [ + "Teaching and Learning Programming", + "Spreadsheets and End-User Computing", + "Software Engineering Research" + ], + "discovered_via": [ + "neighbor:openalex:W4390315357" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1580495313", + "title": "Everyday Irrationality: How Pseudo- Scientists, Lunatics, And The Rest Of Us Systematically Fail To Think Rationally", + "year": 2001, + "url": "http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032154628&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA", + "doi": null, + "abstract": "Robyn Dawes defines irrationality as adhering to beliefs that are inherently self-contradictory, not just incorrect, self-defeating, or the basis of poor decisions. Such beliefs are unfortunately common. Witness two examples: the belief that child sexual abuse can be diagnosed by observing symptoms typically resulting from such abuse, rather than symptoms that differentiate between abused and non-abused children; and the belief that a physical or personal disaster can be understood by studying it alone in-depth rather than by comparing the situation in which it occurred to similar situations where nothing bad happened. This book first demonstrates how such irrationality results from ignoring obvious comparisons. Such neglect is traced to associational and story-based thinking, while true rational judgment requires comparative thinking. Strong emotion--or even insanity--is one reason for making automatic associations without comparison, but as the author demonstrates, a lot of everyday judgment, unsupported professional claims, and even social policy is based on the same kind of irrationality.", + "citation_count": 110, + "topics": [ + "Child and Adolescent Health" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2136289289", + "title": "Disparities in Health Care Access and Receipt of Preventive Services by Disability Type: Analysis of the Medical Expenditure Panel Survey", + "year": 2014, + "url": "https://doi.org/10.1111/1475-6773.12195", + "doi": "https://doi.org/10.1111/1475-6773.12195", + "abstract": "OBJECTIVE: To examine differences in access to health care and receipt of clinical preventive services by type of disability among working-age adults with disabilities. DATA SOURCE: Secondary analysis of Medical Expenditure Panel Survey (MEPS) data from 2002 to 2008. STUDY DESIGN: We conducted cross-sectional logistic regression analyses comparing people with different types of disabilities on health insurance status and type; presence of a usual source of health care; delayed or forgone care; and receipt of dental checkups and cancer screening. DATA COLLECTION: We pooled annualized MEPS data files across years. Our analytic sample consisted of adults (18-64 years) with physical, sensory, or cognitive disabilities and nonmissing data for all variables of interest. PRINCIPAL FINDINGS: Individuals with hearing impairment had better health care access and receipt than people with other disability types. People with multiple types of limitations were especially likely to have health care access problems and unmet health care needs. CONCLUSIONS: There are differences in health care access and receipt of preventive care depending on what type of disability people have. More in-depth research is needed to identify specific causes of these disparities and assess interventions to address health care barriers for particular disability groups.", + "citation_count": 109, + "topics": [ + "Down syndrome and intellectual disability research", + "Dental Health and Care Utilization", + "Chronic Disease Management Strategies" + ], + "discovered_via": [ + "neighbor:openalex:W4404228668" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2009627170", + "title": "Stages in learning new technology: Naive adult email users", + "year": 1995, + "url": "https://doi.org/10.1016/0360-1315(95)00073-9", + "doi": "https://doi.org/10.1016/0360-1315(95)00073-9", + "abstract": "", + "citation_count": 109, + "topics": [ + "Online and Blended Learning", + "Knowledge Management and Sharing", + "Team Dynamics and Performance" + ], + "discovered_via": [ + "neighbor:openalex:W4394591980" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2015667535", + "title": "Causal Inference and Omitted Variable Bias in Financial Aid Research: Assessing Solutions", + "year": 2008, + "url": "https://doi.org/10.1353/rhe.2008.0010", + "doi": "https://doi.org/10.1353/rhe.2008.0010", + "abstract": "This article highlights the problem of omitted variable bias in research on the causal effect of financial aid on college\u2011going. I first describe the problem of self\u2011selection and the resulting bias from omitted variables. I then assess and explore the strengths and weaknesses of random assignment, multivariate regression, proxy variables, fixed effects, difference\u2011in\u2011differences, regression discontinuity, and instrumental variables techniques in addressing the problem. I focus on the intuition, assumptions, and applications of each method in the context of the same research question, providing practical guidance for researchers interested in implementing these approaches.", + "citation_count": 108, + "topics": [ + "Higher Education Research Studies", + "School Choice and Performance", + "Advanced Causal Inference Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4392172846" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W138572701", + "title": "Models of Bounded Rationality: Economic Analysis and Public Policy", + "year": 1984, + "url": "https://www.amazon.com/Models-Bounded-Rationality-Economic-Analysis/dp/0262690861", + "doi": null, + "abstract": "", + "citation_count": 106, + "topics": [ + "Economic Theory and Institutions" + ], + "discovered_via": [ + "neighbor:openalex:W4391830676" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2026702568", + "title": "To Screen or Not to Screen? Using the Internet for Selection Decisions", + "year": 2011, + "url": "https://doi.org/10.1007/s10672-011-9178-y", + "doi": "https://doi.org/10.1007/s10672-011-9178-y", + "abstract": "", + "citation_count": 105, + "topics": [ + "Impact of Technology on Adolescents", + "Social Media and Politics", + "Employer Branding and e-HRM" + ], + "discovered_via": [ + "neighbor:openalex:W4366989571" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1803530966", + "title": "Using social media content for screening in recruitment and selection: pros and cons", + "year": 2015, + "url": "https://doi.org/10.1177/0950017015613746", + "doi": "https://doi.org/10.1177/0950017015613746", + "abstract": "The article considers the arguments that have been made in defence of social media screening as well as issues that arise and may effectively erode the reliability and utility of such data for employers. First, the authors consider existing legal frameworks and guidelines that are present in the UK and the USA, as well as the subsequent ethical concerns that arise when employers access and use social networking content for employment purposes. Second, several arguments in favour of the use of social networking content are made, each of which is considered from several angles, including concerns about impression management, bias and discrimination, data protection and security. Ultimately, the current state of knowledge does not provide a definite answer as to whether information from social networks is helpful in recruitment and selection.", + "citation_count": 103, + "topics": [ + "Employer Branding and e-HRM", + "Social Media in Health Education", + "Names, Identity, and Discrimination Research" + ], + "discovered_via": [ + "neighbor:openalex:W4381684347" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1507980802", + "title": "The Philosophy of Information", + "year": 2013, + "url": "https://doi.org/10.1002/9781118601471.part1", + "doi": "https://doi.org/10.1002/9781118601471.part1", + "abstract": "", + "citation_count": 103, + "topics": [ + "Epistemology, Ethics, and Metaphysics", + "Computability, Logic, AI Algorithms", + "Economic Theory and Institutions" + ], + "discovered_via": [ + "neighbor:openalex:W4391830676" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4385558068", + "title": "How to Create Business Value Through Technological Innovations Using ICCT Underlying Technologies", + "year": 2023, + "url": "https://doi.org/10.47992/ijaeml.2581.7000.0184", + "doi": "https://doi.org/10.47992/ijaeml.2581.7000.0184", + "abstract": "Purpose: Organizations are struggling to sustain and grow in the 21st century due to many challenges and uncertainties while doing their business. Long-term sustaining in the business needs retaining the existing customers and attracting new customers through various strategies for satisfying, delighting, and enlightening existing customers, and creating overwhelming demand through business value creation for attracting new customers. It is a challenge for all the decision-makers to find out how to create business value to retain existing customers and attract new customers. Here, a conceptual and exploratory analysis is made of how the innovations using technology create business value for organizations in general. Methodology: This conceptual analysis uses an exploratory research method. The information is collected using Google, Google Scholar, and Artificial Intelligence GPT search engines using appropriate keywords and prompt engineering respectively and the collected, analysed, compared, evaluated, and interpreted towards creating business value using technology with special emphasis on the use of ICCT underlying technologies. The advantages, benefits, constraints, and disadvantages of business value creation using ICCT underlying technologies for business value creation are listed from stakeholders\u2019 points of view. Outcome: The role of twelve ICCT Underlying Technologies including AI, Blockchain, Business intelligence, Cloud computing, Cyber security, 3D printing, IoT, Quantum computing, Mobile marketing, Information storage technology, Ubiquitous education technology, and VR & AR for Business Value Creation like Innovation and Differentiation, Customer focus, Operational efficiency, Strategic Partnerships and Alliances, Talent Management, Effective Marketing and Branding, Financial Performance and Growth, Sustainability and Corporate Social Responsibility, and Adaptability and Agility. Originality/Value: New knowledge and interpretation are presented on how to create business value for long time sustainability by organizations in every industry. Type o Paper: Exploratory Research Analysis", + "citation_count": 102, + "topics": [ + "Innovations and Analysis in Business and Education", + "Blockchain Technology Applications and Security", + "Smart Systems and Machine Learning" + ], + "discovered_via": [ + "query:AI recruiting business value time to hire cost per hire quality of hire recruiter productivity ROI" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4381684347", + "title": "The Power of Artificial Intelligence in Recruitment: An Analytical Review of Current AI-Based Recruitment Strategies", + "year": 2023, + "url": "https://doi.org/10.26668/businessreview/2023.v8i6.2089", + "doi": "https://doi.org/10.26668/businessreview/2023.v8i6.2089", + "abstract": "Purpose: The aim of this study is to contribute to the understanding of the power of artificial intelligence (AI) in recruitment and to highlight the opportunities and challenges associated with its use. Theoretical framework: This paper provides a comprehensive analytical review of current AI-based recruitment strategies, drawing on both academic research and industry reports. Design/methodology/approach: The paper critically evaluates the potential benefits and drawbacks of using AI in recruitment and assesses the effectiveness of various AI-based recruitment strategies. Findings: The results indicate that AI-based recruitment strategies such as resume screening, candidate matching, video interviewing, chatbots, predictive analytics, gamification, virtual reality assessments, and social media screening offer significant potential benefits for organizations, including improved efficiency, cost savings, and better-quality hires. However, the use of AI in recruitment also raises ethical and legal concerns, including the potential for algorithmic bias and discrimination. Research, Practical & Social implications: The study concludes by emphasizing the need for further research and development to ensure that AI-based recruitment strategies are effective, unbiased, and aligned with ethical and legal standards. Originality/value: The value of the study lies in its comprehensive exploration of AI in recruitment, synthesizing insights from academic and industry perspectives, and assessing the balance of potential benefits against ethical and legal concerns.", + "citation_count": 97, + "topics": [ + "Artificial Intelligence in Healthcare and Education" + ], + "discovered_via": [ + "query:artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1984018499", + "title": "How Polymorphic Warnings Reduce Habituation in the Brain", + "year": 2015, + "url": "https://doi.org/10.1145/2702123.2702322", + "doi": "https://doi.org/10.1145/2702123.2702322", + "abstract": "Research on security warnings consistently points to habituation as a key reason why users ignore security warnings. However, because habituation as a mental state is difficult to observe, previous research has examined habituation indirectly by observing its influence on security behaviors. This study addresses this gap by using functional magnetic resonance imaging (fMRI) to open the \"black box\" of the brain to observe habituation as it develops in response to security warnings. Our results show a dramatic drop in the visual processing centers of the brain after only the second exposure to a warning, with further decreases with subsequent exposures. To combat the problem of habituation, we designed a polymorphic warning that changes its appearance. We show in two separate experiments using fMRI and mouse cursor tracking that our polymorphic warning is substantially more resistant to habituation than conventional warnings. Together, our neurophysiological findings illustrate the considerable influence of human biology on users' habituation to security warnings.", + "citation_count": 97, + "topics": [ + "Deception detection and forensic psychology", + "Safety Warnings and Signage", + "User Authentication and Security Systems" + ], + "discovered_via": [ + "neighbor:openalex:W4392182430" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1481537702", + "title": "Employment Interview Reliability: New meta\u2010analytic estimates by structure and format", + "year": 2013, + "url": "https://doi.org/10.1111/ijsa.12036", + "doi": "https://doi.org/10.1111/ijsa.12036", + "abstract": "This study sought to provide an update on evidence regarding the interrater reliability of employment interviews. Using a final dataset of 125 coefficients with a total sample size of 32,428, our results highlight the importance of taking all three sources of measurement error (random response, transient, and conspect) into account. For instance, the mean interrater reliability was considerably higher for panel interviews than for separate interviews conducted by different interviewers (.74 vs. .44). A strong implication of our findings is that interview professionals should not base perceptions of the psychometric properties of their interview process on interrater estimates that do not include all three sources. A number of directions for future research were identified, including the influence of cues in medium structure panel interviews (e.g., changes in tone or pitch) and the lower than expected reliability for highly structured interviews conducted separately by different interviewers.", + "citation_count": 94, + "topics": [ + "Employer Branding and e-HRM", + "Survey Methodology and Nonresponse", + "Medical Education and Admissions" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1588170973", + "title": "The Five Factor Model of Personality and Evaluation of Drug Consumption Risk", + "year": 2017, + "url": "https://doi.org/10.1007/978-3-319-55723-6_18", + "doi": "https://doi.org/10.1007/978-3-319-55723-6_18", + "abstract": "", + "citation_count": 92, + "topics": [ + "Substance Abuse Treatment and Outcomes", + "Behavioral Health and Interventions", + "Gambling Behavior and Treatments" + ], + "discovered_via": [ + "neighbor:openalex:W4316038168" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2297259217", + "title": "An FDA for Algorithms", + "year": 2016, + "url": "https://doi.org/10.2139/ssrn.2747994", + "doi": "https://doi.org/10.2139/ssrn.2747994", + "abstract": "", + "citation_count": 91, + "topics": [ + "Judicial and Constitutional Studies", + "Law, Economics, and Judicial Systems", + "Legal Systems and Judicial Processes" + ], + "discovered_via": [ + "neighbor:openalex:W4400303336" + ], + "relevance_score": 0.0, + "relevance_reason": "No abstract or topics establish a connection to hiring, and the work predates the publication window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2985390520", + "title": "Investigating American and Chinese Subjects\u2019 explicit and implicit perceptions of AI-Generated artistic work", + "year": 2019, + "url": "https://doi.org/10.1016/j.chb.2019.106186", + "doi": "https://doi.org/10.1016/j.chb.2019.106186", + "abstract": "", + "citation_count": 90, + "topics": [ + "Media Influence and Health", + "Death Anxiety and Social Exclusion", + "Psychology of Moral and Emotional Judgment" + ], + "discovered_via": [ + "neighbor:openalex:W4392871535" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2769550872", + "title": "Visual snow syndrome: what we know so far", + "year": 2017, + "url": "https://doi.org/10.1097/wco.0000000000000523", + "doi": "https://doi.org/10.1097/wco.0000000000000523", + "abstract": "PURPOSE OF REVIEW: We provide an overview of the neurological condition known as visual snow syndrome. Patients affected by this chronic disorder suffer with a pan-field visual disturbance described as tiny flickering dots, which resemble the static noise of an untuned television. RECENT FINDINGS: The term 'visual snow' has only appeared in the medical literature very recently. The clinical features of the syndrome have now been reasonably described and the pathophysiology has begun to be explored. This review focuses on what is currently known about visual snow. SUMMARY: Recent evidence suggests visual snow is a complex neurological syndrome characterized by debilitating visual symptoms. It is becoming better understood as it is systematically studied. Perhaps the most important unmet need for the condition is a sufficient understanding of it to generate and test hypotheses about treatment.", + "citation_count": 89, + "topics": [ + "Hallucinations in medical conditions", + "Visual perception and processing mechanisms", + "Migraine and Headache Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W623806520", + "title": "Die Diffusion von Innovationen in der Telekommunikation", + "year": 1995, + "url": "https://doi.org/10.1007/978-3-642-79868-9", + "doi": "https://doi.org/10.1007/978-3-642-79868-9", + "abstract": "", + "citation_count": 89, + "topics": [ + "ICT Impact and Policies", + "Digital Platforms and Economics", + "Innovation Policy and R&D" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W117178103", + "title": "Transparency in a Human-Machine Context: Approaches for Fostering Shared Awareness/Intent", + "year": 2014, + "url": "https://doi.org/10.1007/978-3-319-07458-0_18", + "doi": "https://doi.org/10.1007/978-3-319-07458-0_18", + "abstract": "", + "citation_count": 87, + "topics": [ + "Human-Automation Interaction and Safety", + "Ethics and Social Impacts of AI", + "Healthcare Technology and Patient Monitoring" + ], + "discovered_via": [ + "neighbor:openalex:W4386142022" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2103154776", + "title": "Social Desirability Response Bias and the Marlowe-Crowne Social Desirability Scale", + "year": 1988, + "url": "https://doi.org/10.2466/pr0.1988.63.1.227", + "doi": "https://doi.org/10.2466/pr0.1988.63.1.227", + "abstract": "The reported studies investigated two issues of importance to researchers and practitioners interested in the Marlowe-Crowne Social Desirability Scale, the sensitivity or social desirability of the individual items comprising the scale and the continued appropriateness of the original scoring scheme presented in 1960. Analysis indicates that over half of the items may no longer be sensitive enough to be useful. Also, it appears that the original keying may no longer be appropriate for a number of items. Finally, based on these findings, implications for the continued use of the Marlowe-Crowne Scale are discussed.", + "citation_count": 86, + "topics": [ + "Survey Sampling and Estimation Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4409825334" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2122565550", + "title": "Gender in the Information Society: Strategies of Inclusion", + "year": 2007, + "url": "https://doi.org/10.1177/097185240701100202", + "doi": "https://doi.org/10.1177/097185240701100202", + "abstract": "This article reports from a European study on efforts to close a gendered digital divide through inclusion. The authors argue that inclusion is not just a mirror image of exclusion, and that to achieve inclusion, it is not sufficient to curb exclusion mechanisms but to enhance positive measures of inclusion. A variety of inclusion strategies have been studied, the authors concluding that \u2018one size does not fit all\u2019. Therefore, to reach a wide audience, a combination of many different strategies is needed. More women users are not sufficient to increase women\u2019s influence on ICT development, however. Particular measures are needed to recruit more women into the ICT profession and to curb marginalization within the profession.", + "citation_count": 83, + "topics": [ + "ICT Impact and Policies", + "Gender and Technology in Education" + ], + "discovered_via": [ + "neighbor:openalex:W4366989571" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4283076195", + "title": "How business intelligence capability impacts decision-making speed, comprehensiveness, and firm performance", + "year": 2022, + "url": "https://doi.org/10.1177/02666669221108438", + "doi": "https://doi.org/10.1177/02666669221108438", + "abstract": "Scholars and practitioners have trumpeted business intelligence (BI) capability as a game-changer due to its significant impact on firm performance. Despite these claims, the amplifying and underlying mechanisms governing the relationship between BI capability and organizational performance are still in their infancy. This research examines the nexus between BI capability, decision-making speed, comprehensiveness, and organizational performance. This study, drawing on knowledge-based theory, proposes a conceptual model to explain how BI capability influences organizational performance through decision-making speed and comprehensiveness and the moderating role of firm size. The proposed moderated-mediated model was tested using survey data from 236 respondents occupying leadership positions in various Jordanian industries. Partial least squares structural equation modeling (PLS-SEM) was used to diagnose the proposed model. BI capability indirectly affects firm performance through decision-making speed and comprehensiveness. These mediating effects do not vary by company size. This paper contributed theoretically and practically to the BI framework considering decision-making, firm performance, and firm size. Implications for theory-building and practice are described.", + "citation_count": 82, + "topics": [ + "Big Data and Business Intelligence", + "Competitive and Knowledge Intelligence", + "Intellectual Capital and Performance Analysis" + ], + "discovered_via": [ + "neighbor:openalex:W4404228668" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2796060246", + "title": "Artificial Intelligence and its Role in Near Future", + "year": 2018, + "url": "https://doi.org/10.48550/arxiv.1804.01396", + "doi": "https://doi.org/10.48550/arxiv.1804.01396", + "abstract": "AI technology has a long history which is actively and constantly changing and growing. It focuses on intelligent agents, which contain devices that perceive the environment and based on which takes actions in order to maximize goal success chances. In this paper, we will explain the modern AI basics and various representative applications of AI. In the context of the modern digitalized world, AI is the property of machines, computer programs, and systems to perform the intellectual and creative functions of a person, independently find ways to solve problems, be able to draw conclusions and make decisions. Most artificial intelligence systems have the ability to learn, which allows people to improve their performance over time. The recent research on AI tools, including machine learning, deep learning and predictive analysis intended toward increasing the planning, learning, reasoning, thinking and action taking ability. Based on which, the proposed research intends towards exploring on how the human intelligence differs from the artificial intelligence. Moreover, we critically analyze what AI of today is capable of doing, why it still cannot reach human intelligence and what are the open challenges existing in front of AI to reach and outperform human level of intelligence. Furthermore, it will explore the future predictions for artificial intelligence and based on which potential solution will be recommended to solve it within next decades.", + "citation_count": 80, + "topics": [ + "Anomaly Detection Techniques and Applications", + "EEG and Brain-Computer Interfaces", + "Currency Recognition and Detection" + ], + "discovered_via": [ + "neighbor:openalex:W2992233710" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2089551710", + "title": "The assessment of end-user training needs", + "year": 1995, + "url": "https://doi.org/10.1145/213859.214793", + "doi": "https://doi.org/10.1145/213859.214793", + "abstract": "article Free Access Share on The assessment of end-user training needs Authors: R. Ryan Nelson Center for the Management of Information Technology, McIntire School of Commerce, The University of Virginia Center for the Management of Information Technology, McIntire School of Commerce, The University of VirginiaView Profile , Ellen M. Whitener McIntire School of Commerce, University of Virginia, Charlottesville, VA McIntire School of Commerce, University of Virginia, Charlottesville, VAView Profile , Henry H. Philcox Internal Revenue Service, Department of the Treasury, Washington, D.C. Internal Revenue Service, Department of the Treasury, Washington, D.C.View Profile Authors Info & Claims Communications of the ACMVolume 38Issue 7July 1995 pp 27\u201339https://doi.org/10.1145/213859.214793Published:01 July 1995Publication History 44citation3,750DownloadsMetricsTotal Citations44Total Downloads3,750Last 12 Months123Last 6 weeks34 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF", + "citation_count": 80, + "topics": [ + "Information Systems Education and Curriculum Development", + "Open Education and E-Learning", + "Spreadsheets and End-User Computing" + ], + "discovered_via": [ + "neighbor:openalex:W4407173730" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2218451112", + "title": "Big Data and the danger of being precisely inaccurate", + "year": 2015, + "url": "https://doi.org/10.1177/2053951715602495", + "doi": "https://doi.org/10.1177/2053951715602495", + "abstract": "Social scientists and data analysts are increasingly making use of Big Data in their analyses. These data sets are often \u201cfound data\u201d arising from purely observational sources rather than data derived under strict rules of a statistically designed experiment. However, since these large data sets easily meet the sample size requirements of most statistical procedures, they give analysts a false sense of security as they proceed to focus on employing traditional statistical methods. We explain how most analyses performed on Big Data today lead to \u201cprecisely inaccurate\u201d results that hide biases in the data but are easily overlooked due to the enhanced significance of the results created by the data size. Before any analyses are performed on large data sets, we recommend employing a simple data segmentation technique to control for some major components of observational data biases. These segments will help to improve the accuracy of the results.", + "citation_count": 79, + "topics": [ + "Data-Driven Disease Surveillance", + "Crime Patterns and Interventions", + "Data Analysis with R" + ], + "discovered_via": [ + "neighbor:openalex:W4386714740" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2075120043", + "title": "Reduction of selection bias in genomewide studies by resampling", + "year": 2005, + "url": "https://doi.org/10.1002/gepi.20068", + "doi": "https://doi.org/10.1002/gepi.20068", + "abstract": "The accuracy of gene localization, the reliability of locus-specific effect estimates, and the ability to replicate initial claims of linkage and/or association have emerged as major methodological concerns in genomewide studies of complex diseases and quantitative traits. To address the issue of multiple comparisons inherent in genomewide studies, the use of stringent criteria for assessing statistical significance has been generally acknowledged as a strategy to control type I error. However, the application of genomewide significance criteria does not take account of the selection bias introduced into parameter estimates, e.g., estimates of locus-specific effect size of disease/trait loci. Some have argued that reliable locus-specific parameter estimates can only be obtained in an independent sample. In this report, we examine statistical resampling techniques, including cross-validation and the bootstrap, applied to the initial sample to improve the estimation of locus-specific effects. We compare them with the naive method in which all data are used for both hypothesis testing and parameter estimation, as well as with the split-sample approach in which part of the data are reserved for estimation. Upward bias of the naive estimator and inadequacy of the split-sample approach are derived analytically under a simple quantitative trait model. Simulation studies of the resampling methods are performed for both the simple model and a more realistic genomewide linkage analysis. Our results suggest that cross-validation and bootstrap methods can substantially reduce the estimation bias, especially when the effect size is small or there is no genetic effect.", + "citation_count": 76, + "topics": [ + "Genetic Mapping and Diversity in Plants and Animals", + "Genetic Associations and Epidemiology", + "Genetic and phenotypic traits in livestock" + ], + "discovered_via": [ + "neighbor:openalex:W4407173730" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1943661034", + "title": "Big Data and Its Exclusions", + "year": 2013, + "url": "https://doi.org/10.2139/ssrn.2293765", + "doi": "https://doi.org/10.2139/ssrn.2293765", + "abstract": "", + "citation_count": 75, + "topics": [ + "Privacy, Security, and Data Protection" + ], + "discovered_via": [ + "neighbor:openalex:W4318407310" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1995462202", + "title": "Gender Bias in Teacher Education Texts", + "year": 2002, + "url": "https://doi.org/10.1177/0022487102053002008", + "doi": "https://doi.org/10.1177/0022487102053002008", + "abstract": "This content analysis investigated the treatment of gender in 23 teacher education textbooks published between 1998 and 2001. Given the decades of research documenting the impact of gender bias in schools, the authors anticipated stronger, fairer texts than those analyzed 20 years ago. Clearly, there has been progress, but it is minimal. Introductory/foundation texts provide slightly more than 7% of content to gender issues; methods texts average little more than 1%. Although a commitment to gender fairness is verbalized in several of these texts, specific resources and strategies to achieve that goal are often absent. Inadequate, stereotypic, and even inaccurate treatment of gender is commonplace. Tomorrow\u2019s teachers may learn the importance of verbalizing support for equity without the expectation that they can actually help make it a reality.", + "citation_count": 74, + "topics": [ + "Gender Roles and Identity Studies", + "Gender Studies in Language", + "Teacher Education and Leadership Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4385952870" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4404254054", + "title": "AI for Decision Support: Balancing Accuracy, Transparency, and Trust Across Sectors", + "year": 2024, + "url": "https://doi.org/10.3390/info15110725", + "doi": "https://doi.org/10.3390/info15110725", + "abstract": "This study seeks to understand the key success factors that underpin efficiency, transparency, and user trust in automated decision support systems (DSS) that leverage AI technologies across industries. The aim of this study is to facilitate more accurate decision-making with such AI-based DSS, as well as build trust through the need for visibility and explainability by increasing user acceptance. This study primarily examines the nature of AI-based DSS adoption and the challenges of maintaining system transparency and improving accuracy. The results provide practical guidance for professionals and decision-makers to develop AI-driven decision support systems that are not only effective but also trusted by users. The results are also important to gain insight into how artificial intelligence fits into and combines with decision-making, which can be derived from research when thinking about embedding systems in ethical standards.", + "citation_count": 67, + "topics": [ + "Explainable Artificial Intelligence (XAI)", + "Big Data and Business Intelligence", + "Artificial Intelligence in Healthcare and Education" + ], + "discovered_via": [ + "query:AI recruitment system human in the loop recruiter decision support workflow architecture monitoring" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1267152930", + "title": "Can we reliably measure the general factor of intelligence (g) through commercial video games? Yes, we can!", + "year": 2015, + "url": "https://doi.org/10.1016/j.intell.2015.08.004", + "doi": "https://doi.org/10.1016/j.intell.2015.08.004", + "abstract": "", + "citation_count": 65, + "topics": [ + "Cognitive Abilities and Testing", + "Personality Traits and Psychology" + ], + "discovered_via": [ + "neighbor:openalex:W4318407310" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4253830102", + "title": "The Social Desirability Response Bias in Ethics Research", + "year": 2012, + "url": "https://doi.org/10.1007/978-94-007-4126-3_9", + "doi": "https://doi.org/10.1007/978-94-007-4126-3_9", + "abstract": "", + "citation_count": 63, + "topics": [ + "Ethics in Business and Education", + "Academic integrity and plagiarism", + "Social and Intergroup Psychology" + ], + "discovered_via": [ + "neighbor:openalex:W4409825334" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2022029407", + "title": "Multiple Dimensions of Procedural Justice: Longitudinal Effects on Selection System Fairness and Test\u2010Taking Self\u2010Efficacy", + "year": 2001, + "url": "https://doi.org/10.1111/1468-2389.00185", + "doi": "https://doi.org/10.1111/1468-2389.00185", + "abstract": "To demonstrate the multidimensionality of test fairness, we examined the reactions of 246 police applicants to two consecutive selection tests (written and video\u2010based) in terms of eight dimensions of fairness. As hypothesized, each test was seen as more fair in terms of certain dimensions. Furthermore, test fairness measured immediately after each test predicted perceptions of overall selection system fairness measured after candidates received their test results and after controlling for applicants\u2019 selection outcomes (i.e., whether they were eligible for further consideration in the selection process). Job\u2010relatedness/content for the video\u2010based test interacted with test score to affect test\u2010taking self\u2010efficacy. Our discussion focuses on the multidimensionality of test fairness, the contribution of these dimensions to overall selection system fairness, and the consideration of these dimensions in selection system design.", + "citation_count": 62, + "topics": [ + "Policing Practices and Perceptions", + "Sexual Assault and Victimization Studies", + "Social and Intergroup Psychology" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2116769230", + "title": "Tell me more: The effects of expressed interest on receptiveness during dialog", + "year": 2010, + "url": "https://doi.org/10.1016/j.jesp.2010.04.012", + "doi": "https://doi.org/10.1016/j.jesp.2010.04.012", + "abstract": "", + "citation_count": 60, + "topics": [ + "Communication in Education and Healthcare", + "Social and Intergroup Psychology", + "Language, Discourse, Communication Strategies" + ], + "discovered_via": [ + "neighbor:openalex:W4407762039" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2064726690", + "title": "Modeling program predictability", + "year": 1998, + "url": "https://doi.org/10.1145/279358.279371", + "doi": "https://doi.org/10.1145/279358.279371", + "abstract": "Basic properties of program predictability -- for both values and control -- are defined and studied. We take the view that program predictability originates at certain points during a program&apos;s execution, flows through subsequent instructions, and then ends at other points in the program. These key components of predictability: generation, propagation, and termination; are defined in terms of a model. The model is based on a graph derived from dynamic data dependences and a predictor.", + "citation_count": 59, + "topics": [ + "Parallel Computing and Optimization Techniques", + "Distributed systems and fault tolerance", + "Distributed and Parallel Computing Systems" + ], + "discovered_via": [ + "neighbor:openalex:W4386142022" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2059173617", + "title": "Multiple identities, multiple-discrimination: A critical review", + "year": 2008, + "url": "https://doi.org/10.1080/13545700802035659", + "doi": "https://doi.org/10.1080/13545700802035659", + "abstract": "Abstract The concept of multiple-discrimination, particularly as found in the labor market, is fast becoming common parlance among policy-making circles. Understanding discrimination is no longer about uncovering simple and dualistic links between two social groups: it is increasingly apparent that the nature and dynamics of discrimination are complex because the multiple positions occupied by people are shaped by numerous social attributes. Economic theory and economists, however, have hardly addressed issues of multiple-discrimination or intersectional discrimination. By surveying the economics literature, from orthodoxy to heterodoxy, this article shows how economists are lagging behind legal and human rights theorists in tackling the issue. A couple of contemporary cases from the UK, those of Aishah Azmi and Nadia Eweida, are used in this largely critical literature survey to show the value of utilizing a multiple-discrimination framework to acknowledge the complexities and nuances of labor market reality.", + "citation_count": 58, + "topics": [ + "Labor Movements and Unions", + "Social Policy and Reform Studies", + "Labor market dynamics and wage inequality" + ], + "discovered_via": [ + "neighbor:openalex:W4386714740" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W946133946", + "title": "The Importance of Being Earnest [In Security Warnings]", + "year": 2013, + "url": "https://doi.org/10.1007/978-3-642-39884-1_5", + "doi": "https://doi.org/10.1007/978-3-642-39884-1_5", + "abstract": "", + "citation_count": 56, + "topics": [ + "Safety Warnings and Signage", + "User Authentication and Security Systems", + "Deception detection and forensic psychology" + ], + "discovered_via": [ + "neighbor:openalex:W4392182430" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4394591980", + "title": "Generative AI in the Wild: Prospects, Challenges, and Strategies", + "year": 2024, + "url": "https://doi.org/10.1145/3613904.3642160", + "doi": "https://doi.org/10.1145/3613904.3642160", + "abstract": "Propelled by their remarkable capabilities to generate novel and engaging content, Generative Artificial Intelligence (GenAI) technologies are disrupting traditional workflows in many industries. While prior research has examined GenAI from a techno-centric perspective, there is still a lack of understanding about how users perceive and utilize GenAI in real-world scenarios. To bridge this gap, we conducted semi-structured interviews with (N = 18) GenAI users in creative industries, investigating the human-GenAI co-creation process within a holistic LUA (Learning, Using and Assessing) framework. Our study uncovered an intriguingly complex landscape: Prospects \u2013 GenAI greatly fosters the co-creation between human expertise and GenAI capabilities, profoundly transforming creative workflows; Challenges \u2013 Meanwhile, users face substantial uncertainties and complexities arising from resource availability, tool usability, and regulatory compliance; Strategies \u2013 In response, users actively devise various strategies to overcome many of such challenges. Our study reveals key implications for the design of future GenAI tools.", + "citation_count": 55, + "topics": [ + "AI in Service Interactions", + "IoT and Edge/Fog Computing", + "Reinforcement Learning in Robotics" + ], + "discovered_via": [ + "query:generative AI recruiting systems candidate communication job descriptions interview assistants" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2787698406", + "title": "Stealing Hyperparameters in Machine Learning", + "year": 2018, + "url": "https://doi.org/10.48550/arxiv.1802.05351", + "doi": "https://doi.org/10.48550/arxiv.1802.05351", + "abstract": "Hyperparameters are critical in machine learning, as different hyperparameters often result in models with significantly different performance. Hyperparameters may be deemed confidential because of their commercial value and the confidentiality of the proprietary algorithms that the learner uses to learn them. In this work, we propose attacks on stealing the hyperparameters that are learned by a learner. We call our attacks hyperparameter stealing attacks. Our attacks are applicable to a variety of popular machine learning algorithms such as ridge regression, logistic regression, support vector machine, and neural network. We evaluate the effectiveness of our attacks both theoretically and empirically. For instance, we evaluate our attacks on Amazon Machine Learning. Our results demonstrate that our attacks can accurately steal hyperparameters. We also study countermeasures. Our results highlight the need for new defenses against our hyperparameter stealing attacks for certain machine learning algorithms.", + "citation_count": 53, + "topics": [ + "Adversarial Robustness in Machine Learning", + "Machine Learning and Data Classification", + "Anomaly Detection Techniques and Applications" + ], + "discovered_via": [ + "neighbor:openalex:W4387789492" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2602382373", + "title": "Hyperparameter optimization to improve bug prediction accuracy", + "year": 2017, + "url": "https://doi.org/10.1109/maltesque.2017.7882014", + "doi": "https://doi.org/10.1109/maltesque.2017.7882014", + "abstract": "Bug prediction is a technique that strives to identify where defects will appear in a software system. Bug prediction employs machine learning to predict defects in software entities based on software metrics. These machine learning models usually have adjustable parameters, called hyperparameters, that need to be tuned for the prediction problem at hand. However, most studies in the literature keep the model hyperparameters set to the default values provided by the used machine learning frameworks. In this paper we investigate whether optimizing the hyperparameters of a machine learning model improves its prediction power. We study two machine learning algorithms: k-nearest neighbours (IBK) and support vector machines (SVM). We carry out experiments on five open source Java systems. Our results show that (i) models differ in their sensitivity to their hyperparameters, (ii) tuning hyperparameters gives at least as accurate models for SVM and significantly more accurate models for IBK, and (iii) most of the default values are changed during the tuning phase. Based on these findings we recommend tuning hyperparameters as a necessary step before using a machine learning model in bug prediction.", + "citation_count": 53, + "topics": [ + "Software Engineering Research", + "Software Reliability and Analysis Research", + "Software Testing and Debugging Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4387789492" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4392182430", + "title": "Farsight: Fostering Responsible AI Awareness During AI Application Prototyping", + "year": 2024, + "url": "https://doi.org/10.1145/3613904.3642335", + "doi": "https://doi.org/10.1145/3613904.3642335", + "abstract": "Prompt-based interfaces for Large Language Models (LLMs) have made prototyping and building AI-powered applications easier than ever before. However, identifying potential harms that may arise from AI applications remains a challenge, particularly during prompt-based prototyping. To address this, we present Farsight, a novel in situ interactive tool that helps people identify potential harms from the AI applications they are prototyping. Based on a user\u2019s prompt, Farsight highlights news articles about relevant AI incidents and allows users to explore and edit LLM-generated use cases, stakeholders, and harms. We report design insights from a co-design study with 10 AI prototypers and findings from a user study with 42 AI prototypers. After using Farsight, AI prototypers in our user study are better able to independently identify potential harms associated with a prompt and find our tool more useful and usable than existing resources. Their qualitative feedback also highlights that Farsight encourages them to focus on end-users and think beyond immediate harms. We discuss these findings and reflect on their implications for designing AI prototyping experiences that meaningfully engage with AI harms. Farsight is publicly accessible at: https://pair-code.github.io/farsight.", + "citation_count": 52, + "topics": [ + "Ethics and Social Impacts of AI", + "Artificial Intelligence in Healthcare and Education", + "Software Engineering Research" + ], + "discovered_via": [ + "query:AI hiring methods causal evaluation field experiment longitudinal quality of hire" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2767437859", + "title": "Recycling Privileged Learning and Distribution Matching for Fairness", + "year": 2017, + "url": "http://hdl.handle.net/10044/1/64239", + "doi": null, + "abstract": "Equipping machine learning models with ethical and legal constraints is a serious issue; without this, the future of machine learning is at risk. This paper takes a step forward in this direction and focuses on ensuring machine learning models deliver fair decisions. In legal scholarships, the notion of fairness itself is evolving and multi-faceted. We set an overarching goal to develop a unified machine learning framework that is able to handle any definitions of fairness, their combinations, and also new definitions that might be stipulated in the future. To achieve our goal, we recycle two well-established machine learning techniques, privileged learning and distribution matching, and harmonize them for satisfying multi-faceted fairness definitions. We consider protected characteristics such as race and gender as privileged information that is available at training but not at test time; this accelerates model training and delivers fairness through unawareness. Further, we cast demographic parity, equalized odds, and equality of opportunity as a classical two-sample problem of conditional distributions, which can be solved in a general form by using distance measures in Hilbert Space. We show several existing models are special cases of ours. Finally, we advocate returning the Pareto frontier of multi-objective minimization of error and unfairness in predictions. This will facilitate decision makers to select an operating point and to be accountable for it.", + "citation_count": 52, + "topics": [ + "Ethics and Social Impacts of AI" + ], + "discovered_via": [ + "neighbor:openalex:W4316038168" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2110063637", + "title": "A MODEL OF OVERCONFIDENCE", + "year": 2009, + "url": "https://doi.org/10.1111/j.1468-0106.2009.00466.x", + "doi": "https://doi.org/10.1111/j.1468-0106.2009.00466.x", + "abstract": "Abstract People use information about their ability to choose tasks. If more challenging tasks provide more accurate information about ability, people who care about and who are risk averse over their perception of their ability will choose tasks that are not sufficiently challenging. Moderate overestimation of ability and overestimation of the precision of initial information leads people to choose tasks that raise expected output (and utility); however, extreme overconfidence leads people to undertake tasks that are excessively challenging. Consistent with our results, psychologists find that moderate overconfidence is both pervasive and advantageous.", + "citation_count": 50, + "topics": [ + "Decision-Making and Behavioral Economics", + "Experimental Behavioral Economics Studies", + "Financial Markets and Investment Strategies" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2766067679", + "title": "Onboarding software developers and teams in three globally distributed legacy projects: A multi\u2010case study", + "year": 2017, + "url": "https://doi.org/10.1002/smr.1921", + "doi": "https://doi.org/10.1002/smr.1921", + "abstract": "Abstract Onboarding is the process of supporting new employees regarding their social and performance adjustment to their new job. Software companies have faced challenges with recruitment and onboarding of new team members, and there is no study that investigates it in a holistic way. In this paper, we conducted a multi\u2010case study to investigate the onboarding of software developers/teams, associated challenges, and areas for further improvement in 3 globally distributed legacy projects. We employed Bauer's model for onboarding to identify the current state of the onboarding strategies employed in each case. We learned that the employed strategies are semi\u2010formalized. Besides, in projects with multiple sites, some functions are executed locally, and the onboarding outcomes may be hard to control. We also learned that onboarding in legacy projects is especially challenging and that decisions to distribute such projects across multiple locations shall be approached carefully. In our cases, the challenges to learn legacy code were further amplified by the project scale and the distance to the original sources of knowledge. Finally, we identified practices that can be used by companies to increase the chances of being successful when onboarding software developers and teams in globally distributed legacy projects.", + "citation_count": 49, + "topics": [ + "Software Engineering Techniques and Practices", + "Outsourcing and Supply Chain Management", + "Knowledge Management and Sharing" + ], + "discovered_via": [ + "neighbor:openalex:W4392871535" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1546646725", + "title": "Language Use and Persuasion: Multiple Roles for Linguistic Styles", + "year": 2011, + "url": "https://doi.org/10.1111/j.1751-9004.2011.00344.x", + "doi": "https://doi.org/10.1111/j.1751-9004.2011.00344.x", + "abstract": "Abstract Language is the most common medium for persuasion. Stylistic variations in how individuals communicate can influence a number of processes and outcomes in a persuasion setting. This article reviews previous research on a number of commonly studied language styles and their influence on persuasion and provides suggestions for future research. First, we provide a review of some of the commonly studied stylistic features and their relation to persuasion. We then use a common multi\u2010process model of persuasion (i.e., the elaboration likelihood model; Advances in Experimental Social Psychology , 1986, New York: Academic Press) to provide an overview of how linguistic styles can influence persuasion. In the final section, we discuss directions for future research on linguistic styles and persuasion.", + "citation_count": 49, + "topics": [ + "Digital Communication and Language", + "Media Influence and Health", + "Linguistics, Language Diversity, and Identity" + ], + "discovered_via": [ + "neighbor:openalex:W4407762039" + ], + "relevance_score": 0.0, + "relevance_reason": "General review of linguistic style and persuasion with no AI hiring context.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4318406331", + "title": "Personnel selection: A review of ways to maximize validity, diversity, and the applicant experience", + "year": 2023, + "url": "https://doi.org/10.1111/peps.12578", + "doi": "https://doi.org/10.1111/peps.12578", + "abstract": "Abstract Personnel Psychology has a long tradition of publishing important research on personnel selection. In this article, we review some of the key questions and findings from studies published in the journal and in the selection literature more broadly. In doing so, we focus on the various decisions organizations face regarding selection procedure development (e.g., use multiple selection procedures, contextualize procedure content), administration (e.g., provide pre\u2010test explanations, reveal target knowledge, skills, abilities, and other characteristics [KSAOs]), and scoring (e.g., weight predictors and criteria, use artificial intelligence). Further, we focus on how these decisions affect the validity of inferences drawn from the procedures, how use of the procedures may affect organizational diversity, and how applicants experience the procedures. We also consider factors such as cost and time. Based on our review, we highlight practical implications and key directions for future research.", + "citation_count": 47, + "topics": [ + "Employer Branding and e-HRM", + "Medical Education and Admissions", + "Gender Diversity and Inequality" + ], + "discovered_via": [ + "query:AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1794941253", + "title": "From Biological Clocks to Unspeakable Inequalities: The Intersectional Positioning of Young Professionals", + "year": 2014, + "url": "https://doi.org/10.1111/1467-8551.12062", + "doi": "https://doi.org/10.1111/1467-8551.12062", + "abstract": "The article examines how gender and age influence the experience of being a professional by drawing on intersectionality as an act of positioning for which different discursive resources are employed. Through interviews with employees at two professional services firms, it is shown how younger men and women make sense of professional experiences. First, the biological clock is used to explain the divergence of career patterns of men and women while ignoring that all women, regardless of actual maternal status, suffer a maternity penalty. Second, individual strategies for overcoming being in a minority are suggested that indicate that the individual rather than societal structures shape chances of success. Finally, generational change is used to argue that gender inequality belongs to a previous generation, which indicates that inequality is becoming unspeakable. The article shows that young professionals position themselves in unique ways with regard to age and gender, which entails emphasizing individual agency over systemic inequalities.", + "citation_count": 47, + "topics": [ + "Gender Diversity and Inequality", + "Work-Family Balance Challenges", + "Gender, Feminism, and Media" + ], + "discovered_via": [ + "neighbor:openalex:W4366989571" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1964787258", + "title": "Admission decisions and performance during medical school", + "year": 1981, + "url": "https://doi.org/10.1097/00001888-198102000-00001", + "doi": "https://doi.org/10.1097/00001888-198102000-00001", + "abstract": "This study compared a group of 24 applicants who were interviewed and accepted at the Yale University School of Medicine but went to other medical schools (AYEs) with a group of 27 applicants who attended the same schools but had been rejected at Yale following an interview and committee deliberation (NAYs). Measures of performance during medical school included scores on Parts I and II of the examinations of the National Board of Medical Examiners and evaluations from the dean's office at each student's medical school. No relationship was found between admission decisions and performance during medical school when AYEs and NAYs attending the same medical schools were compared.", + "citation_count": 47, + "topics": [ + "Medical Education and Admissions", + "Diversity and Career in Medicine", + "Innovations in Medical Education" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4322742241", + "title": "Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from Two Field Experiments on Recruitment in Tech", + "year": 2023, + "url": "https://doi.org/10.2139/ssrn.4370805", + "doi": "https://doi.org/10.2139/ssrn.4370805", + "abstract": "", + "citation_count": 45, + "topics": [ + "Ethics and Social Impacts of AI", + "Experimental Behavioral Economics Studies", + "Employer Branding and e-HRM" + ], + "discovered_via": [ + "query:algorithmic hiring survey employers recruiters applicants adoption trust attitudes" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2063067015", + "title": "Possible unconscious bias in recruitment and promotion and the need to promote equality", + "year": 2011, + "url": "https://doi.org/10.1080/13603108.2011.611833", + "doi": "https://doi.org/10.1080/13603108.2011.611833", + "abstract": "Legislation to outlaw discrimination has existed for over forty years. The Equality Act (2010) states that it is unlawful for an employer to discriminate against a candidate for a job because of their age, disability, race, belief, sexual orientation or gender in any part of the recruitment process--in job descriptions, person specifications, application forms, during interviews, in tests, or in short-listing. Anti-discrimination legislation has no doubt raised awareness and helped to challenge discriminatory behaviour, but is all of this enough to help prevent discrimination, exclusion and inequality? Many have argued more generally that racial inequality in recruitment between minority ethnic groups and the majority white population continues to represent a persistent source of social and economic injustice. After controlling for age, socio-economic status and number of years in education, research shows that minority ethnic groups still face a significant \"net\" disadvantage in terms of gaining access to and remaining in the labour market. In this article, the authors explore possible unconscious bias in recruitment and promotion, and discuss the need to promote equality.", + "citation_count": 45, + "topics": [ + "Names, Identity, and Discrimination Research", + "Discrimination and Equality Law", + "Gender Diversity and Inequality" + ], + "discovered_via": [ + "neighbor:openalex:W4386714740" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2031193551", + "title": "The Interactive Effects of Race, Gender, and Job Type on Job Suitability Ratings and Selection Decisions1", + "year": 2003, + "url": "https://doi.org/10.1111/j.1559-1816.2003.tb02077.x", + "doi": "https://doi.org/10.1111/j.1559-1816.2003.tb02077.x", + "abstract": "The present study examined the main and interactive effects of race, gender, and job type on job suitability ratings and selection decisions. Consistent with the double\u2010advantage additive effects model of race and gender, highly educated Black women were rated as more suitable for jobs and more likely to be selected for jobs requiring high levels of cognitive ability than were comparably educated White men, White women, or Black men. These results suggest that selection decisions might be jointly determined by race, gender, and the nature of a job. The implications of these findings for overcoming biases in employment\u2010related decision making are discussed.", + "citation_count": 44, + "topics": [ + "Social and Intergroup Psychology", + "Cultural Differences and Values", + "Gender Diversity and Inequality" + ], + "discovered_via": [ + "neighbor:openalex:W4366989571" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2936121504", + "title": "Artificial Intelligence: A Technological Prototype in Recruitment", + "year": 2019, + "url": "https://doi.org/10.4236/jssm.2019.123026", + "doi": "https://doi.org/10.4236/jssm.2019.123026", + "abstract": "Purpose: The study is conducted to evaluate the adaptability of artificial intelligence in recruitment and to assess the effect of this technology on the performance of the employees. Design/Methodology/Approach: Standard Multiple Linear regression model is used to predict the performance of the employees and one-way ANOVA is used to compare the artificial intelligence based recruitment with performance indicating variables namely reliability, productivity, Automation, Gamification & Training using SPSS. Snowball sampling method has been adopted for a sample size of 440 respondents working in leading recruitment consultancies in urban Bangalore. Findings: There is a greater association between the recruitment and performance variables when artificial intelligence is adopted as it is significant at 0.001 per cent level and productivity being the maximum. However, the impact of implementing gamification for recruitment doesn\u2019t have a significant impact on the output due to partial significant effect on the adoption as (p = 0.046 Originality/Value: Artificial intelligence has emerged as a boon to the recruiters by automating the repetitive tasks, administrative tasks. Intelligent screening helps in automating resume screening, recruiter Chatbots for real-time candidate engagement, and digitization of interviews. This promotes pro-active strategic decision making better by the recruiters.", + "citation_count": 43, + "topics": [ + "AI and HR Technologies", + "AI in Service Interactions", + "Organizational and Employee Performance" + ], + "discovered_via": [ + "neighbor:openalex:W4381684347" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1834281593", + "title": "Experiencing Recruitment and Selection", + "year": 2007, + "url": "https://openalex.org/W1834281593", + "doi": null, + "abstract": "Experiencing Recruitment and Selection offers readers a unique insight into this life-changing event; one that is very different to the current recruitment literature as it gives a voice to both applicants and recruiters. \\nUsing real-life stories, this book explores applicants' and selectors' experiences of the recruitment and selection process. The author offers original insights into: \\n \\n* differences between internal and external applicants; \\n* perceptions of fairness; \\n* how failure to get appointed influences internal applicants; \\n* how recruiters select for 'fit'; \\n* the reasons why people apply for jobs; \\n* the influence of market forces on selection decisions. \\n \\nEach chapter focuses on a key topic in recruitment and selection and features at least three related stories. The stories are interwoven with analyses that demonstrate the key lessons for practitioners and students. Each chapter concludes with a series of provocative questions and a guide to further reading.", + "citation_count": 43, + "topics": [ + "Employer Branding and e-HRM", + "Human Resource and Talent Management", + "Personality Traits and Psychology" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1845563778", + "title": "How Accurate Are Your Performance Appraisals?", + "year": 2006, + "url": "https://doi.org/10.1177/009102600603500204", + "doi": "https://doi.org/10.1177/009102600603500204", + "abstract": "Accuracy of performance appraisals is very important in public personnel management. We provide \u201cbenchmark\u201d data from the research literature. We also provide a method for correcting for error in the data so practitioners' data may be more accurately evaluated. We also demonstrate the degree to which multiple raters will improve the accuracy of appraisals. Finally, we make suggestions as to how the PPM professional may improve the accuracy of performance ratings based on the research concerning rating versus ranking performance, the supervisors' knowledge of the employee and the number of dimensions being appraised.", + "citation_count": 43, + "topics": [ + "Accounting and Organizational Management" + ], + "discovered_via": [ + "neighbor:openalex:W4385952870" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4389095033", + "title": "Challenges posed by hijacked journals in Scopus", + "year": 2023, + "url": "https://doi.org/10.1002/asi.24855", + "doi": "https://doi.org/10.1002/asi.24855", + "abstract": "Abstract This study presents and explains the phenomenon of indexjacking , which involves the systematic infiltration of hijacked journals into international indexing databases, with Scopus being one of the most infiltrated among these databases. Through an analysis of known lists of hijacked journals, the study identified at least 67 hijacked journals that have penetrated Scopus since 2013. Of these, 33 journals indexed unauthorized content in Scopus and 23 compromised the homepage link in the journal's profile, while 11 did both. As of September 2023, 41 hijacked journals are still compromising the data of legitimate journals in Scopus. The presence of hijacked journals in Scopus is a challenge for scientific integrity due to the legitimization of unreliable papers that have not undergone peer review and compromises the quality of the Scopus database. The presence of hijacked journals in Scopus has far\u2010reaching effects. Papers published in these journals may be cited, and unauthorized content from these journals in Scopus is thus imported into other databases, including ORCID and the WHO COVID\u201019 Research Database. This poses a particular challenge for research evaluation in those countries, where cloned versions of approved journals may be used to acquire publications and verifying their authenticity can be difficult.", + "citation_count": 42, + "topics": [ + "Academic integrity and plagiarism" + ], + "discovered_via": [ + "neighbor:openalex:W4316038168" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1575461229", + "title": "Will the Scientific and Technology Workforce Meet the Requirements of the Federal Government?", + "year": 2004, + "url": "https://doi.org/10.7249/mg118", + "doi": "https://doi.org/10.7249/mg118", + "abstract": "Experts both inside and outside of government have voiced fears that the federal workforce for carrying out scientific, technical, engineering, and mathematics (STEM) activities is aging and may soon face a dwindling labor pool. The authors assess the condition of this workforce, focusing on three main areas: trends in the U.S. STEM workforce overall that might affect the federal STEM workforce, workforce-shaping activities in the federal STEM workforce, and legislative and programmatic mechanisms for influencing that workforce.", + "citation_count": 42, + "topics": [ + "Research, Science, and Academia" + ], + "discovered_via": [ + "neighbor:openalex:W4402876502" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2161303371", + "title": "Persuasion as a Political Concept", + "year": 1984, + "url": "https://doi.org/10.1017/s0007123400003690", + "doi": "https://doi.org/10.1017/s0007123400003690", + "abstract": "This article discusses the notion of \u2018persuasion\u2019 applied to a political method. It proceeds by comparing and contrasting \u2018persuasion\u2019 with concepts within the \u2018power\u2019 family. There are two sorts of justification for such an exploration of \u2018persuasion\u2019, the first positive and the second negative.", + "citation_count": 41, + "topics": [ + "Critical Theory and Philosophy" + ], + "discovered_via": [ + "neighbor:openalex:W4407762039" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4318407310", + "title": "Artificial intelligence and labour market matching", + "year": 2023, + "url": "https://doi.org/10.1787/2b440821-en", + "doi": "https://doi.org/10.1787/2b440821-en", + "abstract": "While still in its infancy, Artificial Intelligence (AI) is increasingly used in labour market matching, whether by private recruiters, public and private employment services, or online jobs boards and platforms. Applications range from writing job descriptions, applicant sourcing, analysing CVs, chat bots, interview schedulers, shortlisting tools, all the way to facial and voice analysis during interviews. While many tools promise to bring efficiencies and cost savings, they could also improve the quality of matching and jobseeker experience, and even identify and mitigate human bias. There are nonetheless some barriers to a greater adoption of these tools. Some barriers relate to organisation and people readiness, while others reflect concerns about the technology and how it is used, including: robustness, bias, privacy, transparency and explainability. The present paper reviews the literature and some recent policy developments in this field, while bringing new evidence from interviews held with key stakeholders.", + "citation_count": 40, + "topics": [ + "Digital Economy and Work Transformation" + ], + "discovered_via": [ + "query:AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3161889546", + "title": "A Case Study of Onboarding in Software Teams: Tasks and Strategies", + "year": 2021, + "url": "https://doi.org/10.1109/icse43902.2021.00063", + "doi": "https://doi.org/10.1109/icse43902.2021.00063", + "abstract": "Developers frequently move into new teams or environments across software companies. Their onboarding experience is correlated with productivity, job satisfaction, and other short-term and long-term outcomes. The majority of the onboarding process comprises engineering tasks such as fixing bugs or implementing small features. Nevertheless, we do not have a systematic view of how tasks influence onboarding. In this paper, we present a case study of Microsoft, where we interviewed 32 developers moving into a new team and 15 engineering managers onboarding a new developer into their team - to understand and characterize developers' onboarding experience and expectations in relation to the tasks performed by them while onboarding. We present how tasks interact with new developers through three representative themes: learning, confidence building, and socialization. We also discuss three onboarding strategies as inferred from the interviews that managers commonly use unknowingly, and discuss their pros and cons and offer situational recommendations. Furthermore, we triangulate our interview findings with a developer survey (N = 189) and a manager survey (N = 37) and find that survey results suggest that our findings are representative and our recommendations are actionable. Practitioners could use our findings to improve their onboarding processes, while researchers could find new research directions from this study to advance the understanding of developer onboarding. Our research instruments and anonymous data are available at https://zenodo.org/record/4455937#.YCOQCs 0lFd.", + "citation_count": 40, + "topics": [ + "Software Engineering Techniques and Practices", + "Software Engineering Research", + "Open Source Software Innovations" + ], + "discovered_via": [ + "neighbor:openalex:W4392871535" + ], + "relevance_score": 0.0, + "relevance_reason": "Studies employee onboarding after hiring, which is explicitly outside scope, and contains no AI.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W3037615466", + "title": "Evaluating and strategizing the onboarding of software developers in large-scale globally distributed projects", + "year": 2020, + "url": "https://doi.org/10.1016/j.jss.2020.110699", + "doi": "https://doi.org/10.1016/j.jss.2020.110699", + "abstract": "", + "citation_count": 40, + "topics": [ + "Software Engineering Techniques and Practices", + "Software Engineering Research", + "Open Source Software Innovations" + ], + "discovered_via": [ + "neighbor:openalex:W4392871535" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3008854194", + "title": "Emerging practices in onboarding programs for PAs and NPs", + "year": 2020, + "url": "https://doi.org/10.1097/01.jaa.0000654016.94204.2e", + "doi": "https://doi.org/10.1097/01.jaa.0000654016.94204.2e", + "abstract": "OBJECTIVE: To describe administrative factors that should be considered when designing an onboarding program for physician assistants (PAs) and NPs. METHODS: Interview study of 13 administrators of onboarding programs. Interviews were transcribed and analyzed for themes by a team of researchers, with feedback from interviewees. RESULTS: Five administrative themes are summarized: goals of onboarding, critical organization factors, organizational fit, designing the program, and costs of onboarding. CONCLUSIONS: Careful planning and administration are essential to the success of onboarding programs. Although onboarding programs must be designed with unique institutional needs in mind, the considerations identified by our project might serve as a useful guide to PA and NP employers in the process of creating an onboarding program.", + "citation_count": 40, + "topics": [ + "Nursing Roles and Practices", + "Interprofessional Education and Collaboration", + "Nursing education and management" + ], + "discovered_via": [ + "neighbor:openalex:W4392871535" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2805558715", + "title": "An Unconscious Drive to Help Others? Using the Implicit Association Test to Measure Prosocial Motivation", + "year": 2018, + "url": "https://doi.org/10.1080/10967494.2018.1471013", + "doi": "https://doi.org/10.1080/10967494.2018.1471013", + "abstract": ":We introduce an Implicit Association Test whose purpose is to measure implicit\u2014or unconscious\u2014prosocial motivation. In doing so, our aim is to provide researchers with a tool that can serve as a complement to explicit, survey-based measures of prosocial and public service motivation. We present the results of a series of tests designed to probe the validity of our version of the Implicit Association Test, which we call the \u201cPro-IAT.\u201d First, we test the Pro-IAT\u2019s known groups\u2019 validity by administering it to four groups of subjects that theory and empirical evidence suggest differ in their prosocial and public service motivation\u2014MPA students, MBA students, Mturk adults, and local government employees. Second, we examine correlations between subjects\u2019 scores on the Pro-IAT and subjects\u2019 survey-based prosocial and public service motivation. Third, we use an online task to test whether subjects\u2019 scores on the Pro-IAT are associated with the amount of (real) money they pledge to charity. Fourth, we use a survey experiment to test whether the Pro-IAT is resistant to a randomly assigned social desirability prime. Our tests provide strong support for the Pro-IAT\u2019s known groups\u2019 validity and modest support for the Pro-IAT\u2019s predictive validity. Additionally, they suggest that the Pro-IAT is resistant to social desirability bias and is measuring a different dimension of prosocial/public service motivation than survey-based measures.", + "citation_count": 40, + "topics": [ + "Social and Intergroup Psychology", + "Experimental Behavioral Economics Studies", + "Nonprofit Sector and Volunteering" + ], + "discovered_via": [ + "neighbor:openalex:W4391598857" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2559579182", + "title": "Aquagenic urticaria: diagnostic and management challenges", + "year": 2016, + "url": "https://doi.org/10.2147/jaa.s91505", + "doi": "https://doi.org/10.2147/jaa.s91505", + "abstract": "Abstract: Aquagenic urticaria (AU) is a rare inducible form of physical urticaria, which occurs in response to cutaneous exposure to water, including sweat and tears. Patients present with characteristic 1\u20133 mm folliculocentric wheals with surrounding 1\u20133 cm erythematous flares within 20\u201330 minutes following skin contact with water. In rare cases, there are concomitant systemic symptoms, such as wheezing or shortness of breath. The pathogenesis of AU is poorly understood at this time, and it appears to be mediated in both a histamine-dependent and independent manner. Diagnosis is based on eliciting a thorough clinical history combined with a water challenge test. Some patients may need to undergo further testing to exclude other physical urticarias. Rarely, multiple physical urticarias can be present in one patient, which can complicate diagnosis and treatment. Currently, the first-line therapy for AU is an oral administration of nonsedating, second-generation H 1 antihistamines, but many patients may require further interventions to have adequate symptomatic control. In this review, we discuss the diagnostic and management challenges of AU. We review the key diagnostic features that differentiate AU from other physical urticarias. We additionally describe a therapeutic ladder for the treatment of AU and the rationale supporting these treatments. Keywords: aquagenic urticaria, physical urticaria, inducible urticaria, diagnosis, management", + "citation_count": 40, + "topics": [ + "Urticaria and Related Conditions", + "Coagulation, Bradykinin, Polyphosphates, and Angioedema", + "Autoimmune Bullous Skin Diseases" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1492591656", + "title": "A Third Theory of Paternalism", + "year": 2015, + "url": "https://doi.org/10.36644/mlr.113.8.third", + "doi": "https://doi.org/10.36644/mlr.113.8.third", + "abstract": "This Article examines the normative significance of paternalism. That an action, a law, or a policy is paternalistic generally counts against it. This Article considers three reasons why this might be so\u2014that is, three theories about what gives paternalism its normative character. This Article\u2019s claim is that the two most common explanations for paternalism\u2019s negative character are mistaken. The first view, which underlies the recent work by Professors Thaler and Sunstein, maintains that paternalism is negatively charged because it involves coercive interference with people\u2019s choices. This approach proves inadequate, however, because more coercive actions can be a less objectionable form of paternalism, and vice versa. Paternalism\u2019s impermissibility varies independently from its coerciveness. The second common theory of paternalism focuses on the distinctive intention behind paternalistic interference. But this approach is ill suited to explain the normative significance of paternalism because permissibility is not generally dependent on intention. This Article sketches a third conception of paternalism\u2014one that locates its normative significance in neither coercion nor motive. This approach maintains that paternalism involves expressive content. Paternalism expresses the idea that the actor knows better than the person acted upon; it implies that the other party is not capable of making good judgments for herself. The normative significance of paternalism derives from the typical impermissibility of making such an expression. That is, paternalism is wrong in the same way that an insult is wrong. This understanding of paternalism\u2019s normative significance provides the tools to make the charge of paternalism leveled against some policies intelligible, and conversely to explain why other paternalistic policies are permissible.", + "citation_count": 40, + "topics": [ + "Political Philosophy and Ethics" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4210820789", + "title": "Beyond The Limits of Predictability in Human Mobility Prediction: Context-Transition Predictability", + "year": 2022, + "url": "https://doi.org/10.1109/tkde.2022.3148300", + "doi": "https://doi.org/10.1109/tkde.2022.3148300", + "abstract": "Urban human mobility prediction is forecasting how people move in cities. It is crucial for many smart city applications including route optimization, preparing for dramatic shifts in modes of transportation, or mitigating the epidemic spread of viruses such as COVID-19. Previous research propose the maximum predictability to derive the theoretical limits of accuracy that any predictive algorithm could achieve on predicting urban human mobility. However, existing maximum predictability only considers the sequential patterns of human movements and neglects the contextual information such as the time or the types of places that people visit, which plays an important role in predicting one's next location. In this paper, we propose new theoretical limits of predictability, namely Context-Transition Predictability, which not only captures the sequential patterns of human mobility, but also considers the contextual information of human behavior. We compare our Context-Transition Predictability with other kinds of predictability and find that it is larger than these existing ones. We also show that our proposed Context-Transition Predictability provides us a better guidance on which predictive algorithm to be used for forecasting the next location when considering the contextual information. Source code is at https://github.com/zcfinal/ContextTransitionPredictability.", + "citation_count": 39, + "topics": [ + "Human Mobility and Location-Based Analysis", + "Urban Transport and Accessibility", + "Transportation Planning and Optimization" + ], + "discovered_via": [ + "neighbor:openalex:W4386142022" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1596643702", + "title": "Cognitive Tests Used in Selection Can Have Content Validity as Well as Criterion Validity: A broader research review and implications for practice", + "year": 2012, + "url": "https://doi.org/10.1111/j.1468-2389.2012.00573.x", + "doi": "https://doi.org/10.1111/j.1468-2389.2012.00573.x", + "abstract": "Many industrial/organizational ( I/O ) psychologists, both academics and practitioners, believe that the content validity model is not appropriate for cognitive ability measures used in personnel selection. They believe that cognitive tests can have criterion validity and construct validity but not content validity. Based on a review of the broader differential psychology research literature on cognitive skills, aptitudes, and abilities, this article demonstrates that with the proper content validity procedures, cognitive ability measures, including, ultimately, some de facto measures of general cognitive ability, can have content validity in addition to criterion and construct validity. Finally, the article considers, critiques, and refutes the specific arguments contending that content validity is inappropriate for use with cognitive skills and abilities. These research facts have implications for I/O practice, professional standards, and legal defensibility of selection programs.", + "citation_count": 39, + "topics": [ + "Psychometric Methodologies and Testing", + "Cognitive Abilities and Testing", + "Advanced Statistical Modeling Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W135177976", + "title": "What? How? Where? A Survey of Crowdsourcing", + "year": 2013, + "url": "https://doi.org/10.1007/978-94-007-7618-0_22", + "doi": "https://doi.org/10.1007/978-94-007-7618-0_22", + "abstract": "", + "citation_count": 38, + "topics": [ + "Mobile Crowdsensing and Crowdsourcing", + "Auction Theory and Applications", + "Expert finding and Q&A systems" + ], + "discovered_via": [ + "neighbor:openalex:W4401533174" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2018488738", + "title": "Astrophysics publications on arXiv, Scopus and Mendeley: a case study", + "year": 2013, + "url": "https://doi.org/10.1007/s11192-013-1215-1", + "doi": "https://doi.org/10.1007/s11192-013-1215-1", + "abstract": "", + "citation_count": 38, + "topics": [ + "scientometrics and bibliometrics research", + "Web visibility and informetrics" + ], + "discovered_via": [ + "neighbor:openalex:W4316038168" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2063072954", + "title": "A Comparative Study of Resident Performance on Standardized Training Examinations and the American Board of Ophthalmology Written Examination", + "year": 2010, + "url": "https://doi.org/10.1016/j.ophtha.2010.03.056", + "doi": "https://doi.org/10.1016/j.ophtha.2010.03.056", + "abstract": "", + "citation_count": 38, + "topics": [ + "Intraocular Surgery and Lenses", + "Ophthalmology and Visual Health Research", + "Retinal and Macular Surgery" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3019157102", + "title": "British Thoracic Society guideline for the use of long-term macrolides in adults with respiratory disease", + "year": 2020, + "url": "https://doi.org/10.1136/bmjresp-2019-000489", + "doi": "https://doi.org/10.1136/bmjresp-2019-000489", + "abstract": "The full British Thoracic Society (BTS) guideline for the use of long-term macrolides in adults with respiratory disease is published in Thorax. The following is a summary of the recommendations and good practice points. The sections referred to in the summary refer to the full guideline. The appendices are available in the full guideline and online appendices are available on the BTS website. This is the first BTS guideline to use the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach as part of the process of guideline development and the guideline was used to pilot the new methodology.", + "citation_count": 37, + "topics": [ + "Chronic Obstructive Pulmonary Disease (COPD) Research", + "Respiratory Support and Mechanisms", + "Heart Failure Treatment and Management" + ], + "discovered_via": [ + "neighbor:openalex:W4407173730" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2025875869", + "title": "Crowdsourcing as a Tool for Knowledge Acquisition in Spatial Planning", + "year": 2014, + "url": "https://doi.org/10.3390/fi6010109", + "doi": "https://doi.org/10.3390/fi6010109", + "abstract": "The term \u201ccrowdsourcing\u201d was initially introduced by Howe in his article \u201cThe Rise of Crowdsourcing\u201d [1]. During the last few years, crowdsourcing has become popular among companies, institutions and universities, as a crowd-centered modern \u201ctool\u201d for problem solving. Crowdsourcing is mainly based on the idea of an open-call publication of a problem, requesting the response of the crowd for reaching the most appropriate solution. The focus of this paper is on the role of crowdsourcing in knowledge acquisition for planning applications. The first part provides an introduction to the origins of crowdsourcing in knowledge generation. The second part elaborates on the concept of crowdsourcing, while some indicative platforms supporting the development of crowdsourcing applications are also described. The third part focuses on the integration of crowdsourcing with certain web technologies and GIS (Geographic Information Systems), for spatial planning applications, while in the fourth part, a general framework of the rationale behind crowdsourcing applications is presented. Finally, the fifth part focuses on a range of case studies that adopted several crowdsourcing techniques.", + "citation_count": 37, + "topics": [ + "Geographic Information Systems Studies", + "Mobile Crowdsensing and Crowdsourcing", + "Data Management and Algorithms" + ], + "discovered_via": [ + "neighbor:openalex:W4401533174" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3120370620", + "title": "Socially Responsible AI Algorithms: Issues, Purposes, and Challenges", + "year": 2021, + "url": "https://doi.org/10.48550/arxiv.2101.02032", + "doi": "https://doi.org/10.48550/arxiv.2101.02032", + "abstract": "In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. Discussions about whether we should (re)trust AI have repeatedly emerged in recent years and in many quarters, including industry, academia, healthcare, services, and so on. Technologists and AI researchers have a responsibility to develop trustworthy AI systems. They have responded with great effort to design more responsible AI algorithms. However, existing technical solutions are narrow in scope and have been primarily directed towards algorithms for scoring or classification tasks, with an emphasis on fairness and unwanted bias. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness and connect major aspects of AI that potentially cause AI's indifferent behavior. In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives. We further discuss how to leverage this framework to improve societal well-being through protection, information, and prevention/mitigation.", + "citation_count": 35, + "topics": [ + "Ethics and Social Impacts of AI", + "Adversarial Robustness in Machine Learning", + "Explainable Artificial Intelligence (XAI)" + ], + "discovered_via": [ + "neighbor:openalex:W2992233710" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1601264015", + "title": "Effects of Organizationally Endorsed Coaching on Performance and Validity of Situational Judgment Tests", + "year": 2015, + "url": "https://doi.org/10.1111/ijsa.12105", + "doi": "https://doi.org/10.1111/ijsa.12105", + "abstract": "There is growing interest in organizationally provided or organizationally endorsed coaching. However, little is known about the effects of such coaching on test scores in operational settings. This study reports on an examination of such a program in the context of the use of a situational judgment test ( SJT ) for medical school admissions. We examine the effects of multiple types of coaching methods on SJT scores and on their construct\u2010related and predictive validities. Results suggest that (1) commercial coaching techniques may not be as effective as previously thought, whereas organizationally provided methods may be more effective, and that (2) the criterion\u2010related validity of the SJT scores is not degraded by the availability of coaching. Generally, this study illustrates that concerns about potential unfairness of coaching can be countered by making effective coaching available to all examinees, in the form of organizationally endorsed coaching.", + "citation_count": 35, + "topics": [ + "Medical Education and Admissions", + "Innovations in Medical Education", + "Diversity and Career in Medicine" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3081288631", + "title": "Democratic Transparency in the Platform Society", + "year": 2020, + "url": "https://doi.org/10.1017/9781108890960.013", + "doi": "https://doi.org/10.1017/9781108890960.013", + "abstract": "Following an host of major scandals, transparency has emerged in recent years as one of the leading accountability mechanisms through which the companies operating global platforms for user-generated content have attempted to regain the trust of the public, politicians, and regulatory authorities. Ranging from Facebook's efforts to partner with academics and create a reputable mechanism for third party data access and independent research to the expanded advertising disclosure tools being built for elections around the world, transparency is playing a major role in current governance debates around free expression, social media, and democracy. This article thus seeks to (a) contextualize the recent implementation of transparency as enacted by platform companies with an overview of the ample relevant literature on digital transparency in both theory and practice; (b) consider the potential positive governance impacts of transparency as a form of accountability in the current political moment; and (c) reflect upon the potential shortfalls of transparency that should be considered by legislators, academics, and funding bodies weighing the relative benefits of policy or research dealing with transparency in this area.", + "citation_count": 31, + "topics": [ + "Social Media and Politics", + "Privacy, Security, and Data Protection", + "E-Government and Public Services" + ], + "discovered_via": [ + "neighbor:openalex:W4386714740", + "neighbor:openalex:W4404254054" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2381517764", + "title": "Concept of Ideal Business & Its Realization Using E-Business Model", + "year": 2015, + "url": "https://doi.org/10.5281/zenodo.61648", + "doi": "https://doi.org/10.5281/zenodo.61648", + "abstract": "In this paper, an attempt is made to identify some of the important characteristics of an Ideal Business in terms of input conditions, system requirements, output conditions and market requirements are identified and analyzed. The paper describes how these characteristics can be achieved in practical business by incorporating electronic online business model using mobile devices called mobile business. The paper also discusses how a small business planned by an entrepreneur can be improved towards an ideal business level by incorporating information communication technology (ICT) components. The conceptual nature of mobile business including major features, value chain, benefits of mobile business from customer\u2019s point of view and comparison of mobile business properties with that of ideal business properties are discussed. The various factors affecting mobile business in terms of the characteristics of Ideal business are studied. Finally, some of the possible products and services which can lead to the concept of Ideal Business are identified and analyzed.", + "citation_count": 31, + "topics": [ + "Innovation and Socioeconomic Development", + "Innovations and Analysis in Business and Education", + "Business Strategies and Innovation" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2075701282", + "title": "A Case Study of Human Resource Practices in Small Hotels in Sweden", + "year": 2012, + "url": "https://doi.org/10.1080/15332845.2012.690683", + "doi": "https://doi.org/10.1080/15332845.2012.690683", + "abstract": "The competitive advantage of organizations in the hotel industry is their human resources. The aim of the authors in this article is to investigate the human resources practices in small hotels in Sweden. They examine the practices of hotels in three main areas of human resource management, namely: hiring, training, and performance evaluation. Although the hotels find their human resources important there has been a lack of attention devoted to the development of human resources systems and processes. Accordingly, the implementation and development of human resources systems and procedures depends on the background of the hotel manager or operator and available financial resources.", + "citation_count": 31, + "topics": [ + "Employer Branding and e-HRM", + "Hospitality and Tourism Education", + "Gender Diversity and Inequality" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734", + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "Studies general hotel HR practices without AI and predates the publication window.", + "heuristic_relevance_score": 1.2, + "heuristic_relevance_reason": "include=none; topic-token-hits=2; exclude=none" + }, + { + "id": "openalex:W1539843387", + "title": "The patentability of algorithms", + "year": 1986, + "url": "https://dl.acm.org/citation.cfm?id=18899", + "doi": null, + "abstract": "", + "citation_count": 31, + "topics": [ + "Law, AI, and Intellectual Property" + ], + "discovered_via": [ + "neighbor:openalex:W4391830676" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2912552846", + "title": "Artificial intelligence: HR friend or foe?", + "year": 2019, + "url": "https://doi.org/10.1108/shr-11-2018-0094", + "doi": "https://doi.org/10.1108/shr-11-2018-0094", + "abstract": "Purpose While the use of artificial intelligence (AI) in the workplace is on the rise, few understand how it will affect our jobs. Will it be a hindrance? A threat? Or the solution to the current productivity dilemma? As with any new, and largely untested, technology, AI brings both challenges and opportunities that we need to be conscious of. Design/methodology/approach The current and potential future implementation of AI technologies at Schneider Electric is assessed. Findings In HR, it is our responsibility to help navigate business leaders towards making the best business decision, often with the use of technology. AI, like analytics before it, has huge potential. Originality/value What we know for sure, is that the development of human talent has become one of the top priorities for global CEOs. With severe talent shortages in the UK, finding the right candidates for the job and investing in their professional development and well-being to keep them for longer look like no-brainers.", + "citation_count": 30, + "topics": [ + "Digital Transformation in Industry", + "Digital Innovation in Industries", + "Big Data and Business Intelligence" + ], + "discovered_via": [ + "neighbor:openalex:W4381684347" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2982321410", + "title": "Johnny's Journey Toward Usable Secure Email", + "year": 2019, + "url": "https://doi.org/10.1109/msec.2019.2933683", + "doi": "https://doi.org/10.1109/msec.2019.2933683", + "abstract": "Since the publication of \"Why Johnny Can't Encrypt,\"10 there has been interest in creating usable, secure email that is adoptable by the general public. In this article, we summarize research from the usable-security community on this topic, identify open problems, and call for more research on usable key management.", + "citation_count": 30, + "topics": [ + "User Authentication and Security Systems", + "Privacy, Security, and Data Protection", + "Personal Information Management and User Behavior" + ], + "discovered_via": [ + "neighbor:openalex:W4392182430" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W574269634", + "title": "Human Resources Management for Hospitality", + "year": 1999, + "url": "https://ci.nii.ac.jp/ncid/BA43772734", + "doi": null, + "abstract": "1. The Human Resources Department. 2. Human Resources Management Challenges. 3. The Legislative Environment. 4. Staffing and the Strategic Human Resources Plan. 5. Recruitment. 6. Selection. 7. Wage and Salary. 8. Benefits. 9. Motivation. 10. Coaching, Counseling, and Discipline. 11. Labor Relations. 12. Orientation. 13. Training. 14. Career Development. 15. Performance Appraisal and Performance Management. 16. Safety and Health. 17. Human Resource Audits. 18. The Human Resources Department's Contribution to Cost Control. 19. Computers and the Human Resources Management Function. 20. Human Resources Management in Transitional Situations. 21. Human Resources Management in a Global Environment. Index.", + "citation_count": 30, + "topics": [ + "Hospitality and Tourism Education", + "Nonprofit Sector and Volunteering", + "Human Resource Development and Performance Evaluation" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734", + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4362655490", + "title": "Scientists' Perspectives on the Potential for Generative AI in their Fields", + "year": 2023, + "url": "https://doi.org/10.48550/arxiv.2304.01420", + "doi": "https://doi.org/10.48550/arxiv.2304.01420", + "abstract": "Generative AI models, including large language models and multimodal models that include text and other media, are on the cusp of transforming many aspects of modern life, including entertainment, education, civic life, the arts, and a range of professions. There is potential for Generative AI to have a substantive impact on the methods and pace of discovery for a range of scientific disciplines. We interviewed twenty scientists from a range of fields (including the physical, life, and social sciences) to gain insight into whether or how Generative AI technologies might add value to the practice of their respective disciplines, including not only ways in which AI might accelerate scientific discovery (i.e., research), but also other aspects of their profession, including the education of future scholars and the communication of scientific findings. In addition to identifying opportunities for Generative AI to augment scientists' current practices, we also asked participants to reflect on concerns about AI. These findings can help guide the responsible development of models and interfaces for scientific education, inquiry, and communication.", + "citation_count": 29, + "topics": [ + "Scientific Computing and Data Management" + ], + "discovered_via": [ + "query:generative AI recruiting systems candidate communication job descriptions interview assistants" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2940029036", + "title": "Developing a Metric of Usable Space for Zoo Exhibits", + "year": 2019, + "url": "https://doi.org/10.3389/fpsyg.2019.00791", + "doi": "https://doi.org/10.3389/fpsyg.2019.00791", + "abstract": "The size of animal exhibits has important effects on their lives and welfare. However, most references to exhibit size only consider floor space and height dimensions, without considering the space afforded by usable features within the exhibit. In this paper, we develop two possible methods for measuring the usable space of zoo exhibits and apply these to a sample exhibit. Having a metric for usable space in place will provide a better reflection of the quality of different exhibits, and enhance comparisons between exhibits.", + "citation_count": 29, + "topics": [ + "Animal Behavior and Welfare Studies", + "Human-Animal Interaction Studies", + "Wildlife Ecology and Conservation" + ], + "discovered_via": [ + "neighbor:openalex:W4392182430" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1489029189", + "title": "Abort, Retry, Fail: Scoping Techno-Regulation and Other Techno-Effects", + "year": 2013, + "url": "https://doi.org/10.1007/978-94-007-6314-2_4", + "doi": "https://doi.org/10.1007/978-94-007-6314-2_4", + "abstract": "", + "citation_count": 29, + "topics": [ + "Ethics and Social Impacts of AI", + "Privacy, Security, and Data Protection", + "Artificial Intelligence in Law" + ], + "discovered_via": [ + "neighbor:openalex:W4394590365" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2030368302", + "title": "Stereotyping as a response strategy when faking personality questionnaires", + "year": 2006, + "url": "https://doi.org/10.1016/j.paid.2005.11.018", + "doi": "https://doi.org/10.1016/j.paid.2005.11.018", + "abstract": "", + "citation_count": 29, + "topics": [ + "Personality Traits and Psychology", + "Social and Intergroup Psychology", + "Names, Identity, and Discrimination Research" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2066240519", + "title": "Two Experiments in Reducing Overconfidence in Spreadsheet Development", + "year": 2007, + "url": "https://doi.org/10.4018/joeuc.2007010101", + "doi": "https://doi.org/10.4018/joeuc.2007010101", + "abstract": "This paper describes two experiments that examined overconfidence in spreadsheet development. Overconfidence has been seen widely in spreadsheet development and could account for the rarity of testing by end-user spreadsheet developers. The first experiment studied a new way of measuring overconfidence. It demonstrated that overconfidence really is strong among spreadsheet developers. The second experiment attempted to reduce overconfidence by telling subjects in the treatment group the percentage of students who made errors on the task in the past. This warning did reduce overconfidence, and it reduced errors somewhat, although not enough to make spreadsheet development safe.", + "citation_count": 27, + "topics": [ + "Spreadsheets and End-User Computing", + "Statistics Education and Methodologies", + "Intelligent Tutoring Systems and Adaptive Learning" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1963550198", + "title": "Business Value Creation through Business Processes Management and Operational Business Intelligence Integration", + "year": 2010, + "url": "https://doi.org/10.1109/hicss.2010.89", + "doi": "https://doi.org/10.1109/hicss.2010.89", + "abstract": "Current approaches to Business Process Management (BPM) and operational Business Intelligence (BI) integration have been very limited and simply reduced to the problem of technical integration between BPM and BI systems. This paper argues that further opportunities for business value creation could be discovered through systematic analysis of the non-technical aspects of BI and BPM integration, especially in terms of strategy alignment, human-centered knowledge management and ongoing improvement of BI supported processes. The paper proposes a theoretical framework founded in the related research in BPM, BI and Knowledge Management (KM) fields and describes how it has been used to guide our empirical case study research in service organisations in the context of BI-supported customer-facing processes.", + "citation_count": 26, + "topics": [ + "Big Data and Business Intelligence", + "Competitive and Knowledge Intelligence", + "Business Process Modeling and Analysis" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "General business-process and intelligence integration with no hiring application and outside the publication window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2991634017", + "title": "Investor Overconfidence in Experimental Asset Markets across Market States", + "year": 2019, + "url": "https://doi.org/10.1080/15427560.2019.1692845", + "doi": "https://doi.org/10.1080/15427560.2019.1692845", + "abstract": "This study explores how individual overconfidence adjusts after receiving extreme feedback that either supports or contradicts previous decision-making when buying or selling stocks. We find that highly contradicting feedback causes overconfidence to vanish as confidence declines sharply while supportive signals cause overconfidence to increase. Further evidence suggests that strong feedback impulses are associated with higher investor disagreement, supporting prior hypotheses that investors interpret such impulses differently. We also find that methodologies that measure overconfidence in prediction tasks systematically overstate confidence scores as respondents tend to fail to internalize stated confidence intervals appropriately.", + "citation_count": 24, + "topics": [ + "Financial Markets and Investment Strategies", + "Decision-Making and Behavioral Economics", + "Forecasting Techniques and Applications" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2169196470", + "title": "Bigger is Not Always Better: Should Educators Aim For Big Transformative Learning Events or Small Transformative Experiences?", + "year": 2015, + "url": "https://jotl.uco.edu/index.php/jotl/article/download/51/49", + "doi": null, + "abstract": "Facilitating transformative learning is a praiseworthy goal among educators who want to make a significant impact on the lives of their students. Transformative learning is typically defined as involving a fundamental shift in students\u2019 worldviews and/or identity. While we agree that teachers should retain such important goals, we argue that facilitating transformative learning is difficult for many reasons. We then suggest that a more manageable task is to use existing instructional techniques to generate small-scale transformation in the form of transformative experience (TE). Specifically, TE can be used to create micro changes in student perspectives. Transformative experiences are more manageable in the typical classroom and an accumulation of small changes can lead to the type of transformative learning that influences student identity.", + "citation_count": 23, + "topics": [ + "Adult and Continuing Education Topics", + "Education and Critical Thinking Development", + "Critical and Liberation Pedagogy" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3199750033", + "title": "Consensus and (lack of) accuracy in perceptions of avatar trustworthiness", + "year": 2021, + "url": "https://doi.org/10.1016/j.chb.2021.107017", + "doi": "https://doi.org/10.1016/j.chb.2021.107017", + "abstract": "In some online interactions, people use avatars to represent themselves and judge whether interaction partners should be trusted. However, little is known about human accuracy in perceptions of avatar trustworthiness. We conducted a two-stage study to investigate whether people are able to accurately judge trustworthiness in avatars. In Stage 1, participants created avatars using avatarmaker.com and made decisions as trustees in an incentivized trust game (N = 360 avatars). In Stage 2, a new group of participants (N = 315 raters) were presented with avatars created in Stage 1; rated their trustworthiness; and made decisions about whether to trust them. We observed three main results: First, there was a high level of consensus in perceptions of avatar trustworthiness. Differences in trustworthiness judgments were mainly attributed to differences between avatars (rather than differences between raters). Second, raters' trust decisions were guided by their perceptions of avatar trustworthiness. Third, perceptions of avatar trustworthiness were not associated with actual trustworthiness of avatar creators. People were not, on average, able to accurately identify the actual trustworthiness of avatar creators from avatar appearance. Our results suggest that people erroneously rely on others\u2019 avatar appearance in online interactions.", + "citation_count": 22, + "topics": [ + "Evolutionary Psychology and Human Behavior", + "Psychology of Moral and Emotional Judgment", + "Face Recognition and Perception" + ], + "discovered_via": [ + "neighbor:openalex:W4381848566" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2021684799", + "title": "Wild in the Laboratory", + "year": 2013, + "url": "https://doi.org/10.1145/2491500.2491507", + "doi": "https://doi.org/10.1145/2491500.2491507", + "abstract": "Suchman\u2019s book Plans and Situated Actions has been influential in HCI (Human Computer Interaction). The book is often discussed with reference to ethnographic fieldwork, sometimes being cited as if it were a field study. However, the book uses examples from a laboratory study and contains criticisms of ethnography. This article explores how and why Suchman carried out a laboratory study. Based upon this exploration, it argues that social analysis in HCI does not necessitate fieldwork outside the laboratory. More broadly, the paper argues that an appreciation of Plans and Situated Actions can help in moving towards forms of social analysis that span both the laboratory and the world outside. If there is to be a \u201cturn to the wild\u201d in HCI, this should not be a turn away from the laboratory but a turn away from research methods that ignore human practice. This is not to defend laboratory experiments, but to defend laboratory-based studies that explicate technology in practice.", + "citation_count": 22, + "topics": [ + "Innovative Human-Technology Interaction", + "Interactive and Immersive Displays", + "Usability and User Interface Design" + ], + "discovered_via": [ + "neighbor:openalex:W4394591980" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1548465722", + "title": "Stimulating Creative Rationality to Stimulate Innovation", + "year": 2011, + "url": "https://doi.org/10.1111/j.1467-8691.2011.00603.x", + "doi": "https://doi.org/10.1111/j.1467-8691.2011.00603.x", + "abstract": "The present contribution examines how the Western tradition of thought has pushed \u2018creative rationality\u2019 to one side and how rediscovering, that is to say, reintegrating this type of rationality provides a way of stimulating creativity and innovation. Exploring creative rationality has indeed great practical implications. It leads to promotion of a \u2018pedagogy of adventure\u2019.", + "citation_count": 21, + "topics": [ + "Creativity in Education and Neuroscience", + "Design Education and Practice", + "Cognitive Science and Mapping" + ], + "discovered_via": [ + "neighbor:openalex:W4391830676" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2050896831", + "title": "A neural network modelling on human resource talent selection", + "year": 2001, + "url": "https://doi.org/10.1504/ijhrdm.2001.001006", + "doi": "https://doi.org/10.1504/ijhrdm.2001.001006", + "abstract": "Due to the rapid improvement of information technologies (ITs), they have been applied in the area of human resources management (HRM), which is called a human resources information system (HRIS). An effective HRIS should be able to handle complicated personnel data to assist the implementation of new policies and managerial strategies in an organisation. Thus, the main purpose of this study is to apply an artificial neural network (ANN), which is capable of learning and recalling and has been widely used in the areas of engineering, for top managers to select potential employees for the position of manager. The case study results show that the proposed system is well able to learn the data collected from the top managers and the test results are very promising. Also, the proposed system has been implemented in the Web in order to fit the requirements of the electronic era.", + "citation_count": 21, + "topics": [ + "AI and HR Technologies" + ], + "discovered_via": [ + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2931662336", + "title": "Personality correlates and utilitarian judgments in the everyday context: Psychopathic traits and differential effects of empathy, social dominance orientation, and dehumanization beliefs", + "year": 2019, + "url": "https://doi.org/10.1016/j.paid.2019.03.029", + "doi": "https://doi.org/10.1016/j.paid.2019.03.029", + "abstract": "", + "citation_count": 20, + "topics": [ + "Psychology of Moral and Emotional Judgment", + "Death Anxiety and Social Exclusion", + "Personality Traits and Psychology" + ], + "discovered_via": [ + "neighbor:openalex:W4394590365", + "neighbor:openalex:W4404228668" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2238629437", + "title": "Letters to the Editor", + "year": 2015, + "url": "https://doi.org/10.1609/aimag.v36i4.2621", + "doi": "https://doi.org/10.1609/aimag.v36i4.2621", + "abstract": "Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents \u2014 systems that perceive and act in some environment. In this context, \"intelligence\" is related to statistical and economic notions of rationality \u2014 colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic and decision-theoretic representations and statistical learning methods has led to a large degree of integration and cross-fertilization among AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems. As capabilities in these areas and others cross the threshold from laboratory research to economically valuable technologies, a virtuous cycle takes hold whereby even small improvements in performance are worth large sums of money, prompting greater investments in research. There is now a broad consensus that AI research is progressing steadily, and that its impact on society is likely to increase. The potential benefits are huge, since everything that civilization has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools AI may provide, but the eradication of disease and poverty are not unfathomable. Because of the great potential of AI, it is important to research how to reap its benefits while avoiding potential pitfalls. The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. Such considerations motivated the AAAI 2008\u201309 Presidential Panel on Long-Term AI Futures and other projects on AI impacts, and constitute a significant expansion of the field of AI itself, which up to now has focused largely on techniques that are neutral with respect to purpose. We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do. The attached research priorities document [see page X] gives many examples of such research directions that can help maximize the societal benefit of AI. This research is by necessity interdisciplinary, because it involves both society and AI. It ranges from economics, law and philosophy to computer security, formal methods and, of course, various branches of AI itself. In summary, we believe that research on how to make AI systems robust and beneficial is both important and timely, and that there are concrete research directions that can be pursued today.", + "citation_count": 20, + "topics": [ + "Knowledge Management and Technology", + "Computational and Text Analysis Methods" + ], + "discovered_via": [ + "neighbor:openalex:W2992233710" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W652989322", + "title": "Human Law and Computer Law: Comparative Perspectives", + "year": 2013, + "url": "https://doi.org/10.1007/978-94-007-6314-2", + "doi": "https://doi.org/10.1007/978-94-007-6314-2", + "abstract": "", + "citation_count": 20, + "topics": [ + "Law, AI, and Intellectual Property" + ], + "discovered_via": [ + "neighbor:openalex:W4394590365" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2170898289", + "title": "Use of machine learning for continuous improvement of the real time heterarchical manufacturing control system performances", + "year": 2008, + "url": "https://doi.org/10.1504/ijise.2008.017555", + "doi": "https://doi.org/10.1504/ijise.2008.017555", + "abstract": "Heterarchic manufacturing control system offer a significant potential in terms of capacity, adaptation, self-organisation and real time control for dynamic manufacturing system. In this paper, we present our steps to work out a manufacturing control system where the decisions taken by the system are the result of an agents group work, these agents ensure a continuous improvement of these performance, thanks to the reinforcement learning technique which was introduced to them. This technique of learning makes it possible for the agents to learn the best behaviour in their various roles (answer the requests (risks), self-organisation, plan, etc.) without attenuating the system real time quality. We also introduce a new type of agents called 'observant agent', which has the responsibility to supervise the evolution of the system's total performance. A computer implementation and experimentation of this model are provided in this paper to demonstrate the contribution of our approach.", + "citation_count": 20, + "topics": [ + "Scheduling and Optimization Algorithms", + "Elevator Systems and Control", + "Advanced Manufacturing and Logistics Optimization" + ], + "discovered_via": [ + "neighbor:openalex:W4391598857" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3133416822", + "title": "Social Sustainability on Corporate Boards: The Effects of Female Family Members on R&D", + "year": 2021, + "url": "https://doi.org/10.3390/su13041982", + "doi": "https://doi.org/10.3390/su13041982", + "abstract": "There is growing institutional and social pressure for greater balance, parity, and equality at the highest levels of corporations. This is coupled with an increasing interest in analysing the effects of gender diversity on corporate boards. However, companies may only reap the benefits of gender diversity by achieving better qualified and more independent boards. This study aims to contribute to the open debate on the effects of board gender diversity on R&D, by taking into account the independence of female directors. Panel regression analyses were performed with data for 67 Spanish-listed companies during the 2003\u20132019 period. Our results confirm the positive effects of gender diversity on R&D. However, this positive influence is lower if female directors have family links with male members on the board. These findings have policy implications, regarding the need to increase gender equality in corporate boards for social and sustainability purposes, while the benefits are conditioned by the independence of female directors. The value of this research rests on the study of the effects, beyond the mere analysis of financial performance of the gender diversity of boards.", + "citation_count": 19, + "topics": [ + "Gender Diversity and Inequality", + "Corporate Finance and Governance", + "Family Business Performance and Succession" + ], + "discovered_via": [ + "neighbor:openalex:W4322742241" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2775388773", + "title": "Two sources of overconfidence: Incorporating disconfirming feedback in an entrepreneurial context", + "year": 2017, + "url": "http://libjournals.mtsu.edu/index.php/jsbs/article/view/702", + "doi": null, + "abstract": "The prevalence of overconfidence when making entrepreneurial decisions has led some scholars to argue that it has positive ramifications while others to disagree. The question is: Will highly overconfident individuals who consider starting a venture be willing to correct initial misconceptions? We found that it depends upon the source of overconfidence. Even at equally high overconfidence levels, individuals who reached that level by intentionally processing additional evidence were less likely to revise erroneous beliefs when compared to those with a natural tendency toward high overconfidence. In contrast to the overconfidence source, overconfidence level was not associated with changing incorrect beliefs.", + "citation_count": 19, + "topics": [ + "Innovations in Educational Methods", + "Entrepreneurship Studies and Influences", + "Experimental Behavioral Economics Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W256618471", + "title": "The Concept of Validity and the Process of Validation", + "year": 2012, + "url": "https://doi.org/10.1093/oxfordhb/9780199732579.013.0006", + "doi": "https://doi.org/10.1093/oxfordhb/9780199732579.013.0006", + "abstract": "Abstract In this chapter we first set the stage by focusing on the concept of validity, documenting key changes over time in how the term is used and examining the specific ways in which the concept is instantiated in the domain of personnel selection. We then move from conceptual to operational and discuss issues in the use of various strategies to establish what we term the predictive inference, namely, that scores on the predictor measure of interest can be used to draw inferences about an individual's future job behavior or other criterion of interest. Finally, we address a number of specialized issues aimed at illustrating some of the complexities and nuances of validation.", + "citation_count": 19, + "topics": [ + "Psychometric Methodologies and Testing", + "Evaluation and Performance Assessment", + "Qualitative Comparative Analysis Research" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4387789492", + "title": "Fairness-Aware Multimodal Learning in Automatic Video Interview Assessment", + "year": 2023, + "url": "https://doi.org/10.1109/access.2023.3325891", + "doi": "https://doi.org/10.1109/access.2023.3325891", + "abstract": "With the ever-growing reliance on Artificial Intelligence (AI) across diverse domains, there is an increasing concern surrounding the possibility of biases and unfairness inherent in AI systems. Fairness problems in automatic interview assessment systems, especially video-based automated interview assessments, have less been addressed despite their prevalence in the recruiting field. In this paper, we propose a method that resolves fairness problems in an automated interview assessment system that uses multimodal data as input. This is mainly done by minimizing theWasserstein distance between two sensitive groups by introducing a regularization term. Subsequently, we employ a hyperparameter that can control the trade-off between fairness and accuracy. To test our method in various data settings, we suggest a preprocessing method that can manually adjust the underlying degree of unfairness in the training data. Experimental results show that our method presents state-of-the-art results in terms of fairness compared to previous methods.", + "citation_count": 18, + "topics": [ + "Adversarial Robustness in Machine Learning", + "Mobile Crowdsensing and Crowdsourcing", + "Ethics and Social Impacts of AI" + ], + "discovered_via": [ + "query:AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3067217073", + "title": "Emerging practices in onboarding programs for PAs", + "year": 2020, + "url": "https://doi.org/10.1097/01.jaa.0000694968.61482.e6", + "doi": "https://doi.org/10.1097/01.jaa.0000694968.61482.e6", + "abstract": "OBJECTIVE: Healthcare organizations are interested in creating onboarding programs to assist new PA and NP employees as they transition to practice settings. There is a need for information on what the ideal content and topic areas are for clinics to include in their onboarding programs. METHODS: In 2018, we interviewed 13 administrators of onboarding programs. Interviews were transcribed and analyzed for themes by a team of researchers, with feedback from interviewees. RESULTS: Eight content areas were identified and described in this paper: culture and values; goals and strategies; task proficiency; rules and policies; structure; working relationships; wellness; and navigation and politics. CONCLUSIONS: These common content areas may provide a starting point for organizations designing onboarding programs for their unique organizations.", + "citation_count": 18, + "topics": [ + "Nursing Roles and Practices", + "Nursing education and management", + "Workplace Health and Well-being" + ], + "discovered_via": [ + "neighbor:openalex:W4392871535" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3113058030", + "title": "Emerging practices in onboarding programs for PAs", + "year": 2020, + "url": "https://doi.org/10.1097/01.jaa.0000723932.21395.74", + "doi": "https://doi.org/10.1097/01.jaa.0000723932.21395.74", + "abstract": "OBJECTIVE: This is the final article in a series that examines the role of onboarding programs for new physician assistants (PAs) and NPs. On-the-job learning is highly relevant for this workforce. Here we examine the strategies organizations use to impart information and skills in onboarding programs. METHODS: In 2018, we interviewed 13 administrators of onboarding programs. Interviews were transcribed and analyzed for themes by a team of researchers, with feedback from interviewees. RESULTS: Seven strategies were identified and are described in this article: Clinical mentoring, personal and professional mentoring, meeting with/shadowing other professionals, checking in by administrators, delivering didactic content, tailoring content or ramp-up, and assessing/ensuring competency. CONCLUSIONS: This article describes commonly used strategies in onboarding programs for PAs and NPs and can provide guidance to those designing their own onboarding programs. The programs we examined relied heavily on mentoring and other strategies appropriate for adult learners. Future work should evaluate the effectiveness of onboarding programs.", + "citation_count": 18, + "topics": [ + "Innovations in Medical Education", + "Mentoring and Academic Development", + "Nursing education and management" + ], + "discovered_via": [ + "neighbor:openalex:W4392871535" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4230248664", + "title": "Recruitment AI has a Disability Problem: questions employers should be asking to ensure fairness in recruitment", + "year": 2020, + "url": "https://doi.org/10.31235/osf.io/emwn5", + "doi": "https://doi.org/10.31235/osf.io/emwn5", + "abstract": "Artificial Intelligence (AI) technologies have the potential to dramatically impact the lives and life chances of people with disabilities seeking employment and throughout their career progression. While these systems are marketed as highly capable and objective tools for decision making, a growing body of research demonstrates a record of inaccurate results as well as inherent disadvantages for women and people of colour (Broussard, 2018; Noble, 2018; O\u2019Neil 2017). Assessments of fairness in Recruitment AI for people with disabilities have thus far received little attention or have been overlooked (Guo et al., 2019; Petrick, 2015; Trewin, 2018; Trewin et al. 2019; Whittaker et al., 2019). This white paper details the impacts to and concerns of disabled employment seekers using AI systems for recruitment, and provides recommendations on the steps employers can take to ensure innovation in recruitment is also fair to all users. In doing so, we further the point that making systems fairer for disabled employment seekers ensures systems are fairer for all.", + "citation_count": 18, + "topics": [ + "Retirement, Disability, and Employment" + ], + "discovered_via": [ + "neighbor:openalex:W4404228668" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2036165503", + "title": "Distinction Bias in Applicant Reactions to Using Diversity Information in Selection", + "year": 2009, + "url": "https://doi.org/10.1111/j.1468-2389.2009.00480.x", + "doi": "https://doi.org/10.1111/j.1468-2389.2009.00480.x", + "abstract": "Two studies investigated applicant reactions to alternative methods of incorporating diversity in employee selection decisions. Of particular interest was the hypothesized interaction between specific approach to incorporating diversity in the selection policy (holistic vs mechanical) and mode of receiving information about this approach (viewing policies in isolation vs viewing policies simultaneously). Results indicate preference for holistic approaches to incorporating diversity in selection. The magnitude of the preference was much greater when participants evaluated both approaches simultaneously. Results are consistent with the distinction bias, which suggests that people differentiate between options more when they consider them simultaneously than when they view them separately. Findings have implications for applicant reactions research, human resource decision making, and policy formation.", + "citation_count": 18, + "topics": [ + "Gender Diversity and Inequality", + "Social and Intergroup Psychology", + "Job Satisfaction and Organizational Behavior" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1978560125", + "title": "Uses and Abuses of Psychology", + "year": 1955, + "url": "https://doi.org/10.1176/ajp.111.9.715", + "doi": "https://doi.org/10.1176/ajp.111.9.715", + "abstract": "", + "citation_count": 18, + "topics": [ + "Child and Adolescent Psychosocial and Emotional Development", + "Psychotherapy Techniques and Applications" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W24250002", + "title": "Growth and Structure of Workforce in India", + "year": 2016, + "url": "https://doi.org/10.1177/0019466216652753", + "doi": "https://doi.org/10.1177/0019466216652753", + "abstract": "The present article examines the trend in growth of workforce and the change in its structure in India during the last three decades using census data. The analysis indicates that there was a fast decelerating rate of growth in overall workforce, particularly females, between 2001 and 2011. But the work participation rate did not decline, if not increased, as the rate of growth in workforce was not less than that in population. Secondly, incremental workforce, especially the male, reduced to marginal workers category, whereas the high concentration of female in the category of marginal workers slightly reduced. Occupational distribution of workforce shows that cultivators were declining. Such a decline in agriculture was replaced by increasing agricultural labour. The study further shows that growth of workforce in non-agriculture was higher than that in agriculture. Growth of female workers engaged in non-agriculture was higher than their male counterparts. JEL Classification: J01, J08, J21, J82", + "citation_count": 17, + "topics": [ + "Indian Economic and Social Development", + "Agricultural risk and resilience", + "Social and Economic Development in India" + ], + "discovered_via": [ + "neighbor:openalex:W4402876502", + "neighbor:openalex:W4407192387" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4225274761", + "title": "Inclusion done differently? Representations of inclusion and exclusion in the discourse of alternative organizations", + "year": 2022, + "url": "https://doi.org/10.1177/13505084221085902", + "doi": "https://doi.org/10.1177/13505084221085902", + "abstract": "This paper brings together two separate fields\u2014inclusion and alternative organizations\u2014to study the relational aspects of inclusion and exclusion both within and beyond organizations. By analyzing reports and websites of organizations committed to the network \u201cEconomy for the Common Good,\u201d we empirically explore, first, how these organizations represent their \u201calternativeness\u201d and how this relates to inclusion and exclusion; and second, we address the question of who is included in these alternative ventures by examining both their inclusionary and exclusionary potential in terms of diversity. Introducing a conceptual framework to distinguish between internal and external perspectives on inclusion and exclusion, our qualitative analysis reveals the simultaneity of two contradictory phenomena: On the one hand, these alternative organizations offer a new inclusionary potential that encompasses both the social and natural environments; on the other, they tend to ignore internal and external inclusion along diversity dimensions. Hence, we conclude that while there exists the potential to link inclusion with alternative organizations, a commitment to an alternative economy does not automatically lead to an engagement with issues of diversity and inclusion.", + "citation_count": 16, + "topics": [ + "Management and Organizational Studies", + "Corporate Social Responsibility Reporting", + "Nonprofit Sector and Volunteering" + ], + "discovered_via": [ + "neighbor:openalex:W4366989571" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2120516750", + "title": "Bibliometric factors associated with h-index of Peruvian researchers with publications indexed on Web of Science and Scopus databases", + "year": 2014, + "url": "https://doi.org/10.1590/0103-37862014000200004", + "doi": "https://doi.org/10.1590/0103-37862014000200004", + "abstract": "The objective of this article is: a) to identify Peruvian researchers with high, medium and low impact factor according to Web of Science and Scopus databases; b) to identify the bibliometric factor with the highest influence on h-index of Peruvian esearchers; c) to compare h-index between Web of Science and Scopus, at an individual and institutional level. Data were collected from Web of Science and Scopus (189 Peruvian researchers, 28 institutions on Web of Science and 33 on Scopus), between September 1823, 2013. Then, institutional registries were created and linear regression analysis with stepwise procedure was run to identify bibliometric factors with higher influence on the h-index of Peruvian researchers. Web of Science and Scopus showed interesting simmilarities in the h-index of Peruvian academic institutions. At individual level, documents indexed in citation database had the highest influence on the h-index. Regression model identified bibliometric factors with higher influence on the h-index of Peruvian researchers, however further large scale studies are needed to improve external validity.", + "citation_count": 16, + "topics": [ + "scientometrics and bibliometrics research", + "Psychology Research and Bibliometrics", + "Scientific Research and Technology" + ], + "discovered_via": [ + "neighbor:openalex:W4316038168" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1570886148", + "title": "Levels of automation in a simulated failure detection task", + "year": 2004, + "url": "https://doi.org/10.1109/icsmc.2003.1244194", + "doi": "https://doi.org/10.1109/icsmc.2003.1244194", + "abstract": "Systems increasingly provide operators with the ability to move between different levels of automation from entirely manual modes, over partly automated modes (in which some task components are performed by the operator) and up to fully automated modes. It is still unclear what determines the relative efficiency of different levels of automation. An experimental system was developed to study these issues. In the system operators had to detect faulty items, based on partial information. Two levels of automation were tested: aided detection (operators received cues from a failure detection system), and approval (operators could change decisions of the failure detection system). An experiment assessed operators' performance with the different levels of automation and with low and high validity failure detection systems. Results showed that operators adjusted their response to the diagnostic value of the automation. Also, operators responded more strongly to low-validity cues in the approval condition than in the aided detection condition. These results point to some of the complex issues that need to be considered when choosing the appropriate level of automation for a system.", + "citation_count": 16, + "topics": [ + "Human-Automation Interaction and Safety", + "Occupational Health and Safety Research", + "Safety Warnings and Signage" + ], + "discovered_via": [ + "neighbor:openalex:W4318407310" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4254065731", + "title": "Modeling program predictability", + "year": 2002, + "url": "https://doi.org/10.1109/isca.1998.694764", + "doi": "https://doi.org/10.1109/isca.1998.694764", + "abstract": "Basic properties of program predictability-for both values and control-are defined and studied. We take the view that program predictability originates at certain points during a program's execution, flows through subsequent instructions, and then ends at other points in the program. These key components of predictability: generation, propagation, and termination; are defined in terms of a model. The model is based on a graph derived from dynamic data dependences and a predictor. Using the SPEC95 benchmarks, we analyze the predictability phenomena both separately and in combination. Examples are provided to illustrate relationships between model-based characteristics and program constructs. It is shown that most predictability derives from program control structure and immediate values, not program input data. Furthermore, most predictability originates from a relatively small number of generate points. The analysis of obtained results suggests a number of ramifications regarding predictability and its use.", + "citation_count": 16, + "topics": [ + "Parallel Computing and Optimization Techniques", + "Distributed systems and fault tolerance", + "Radiation Effects in Electronics" + ], + "discovered_via": [ + "neighbor:openalex:W4386142022" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3199802296", + "title": "Localizing transparency and accountability in extractives", + "year": 2021, + "url": "https://doi.org/10.1016/j.exis.2021.100991", + "doi": "https://doi.org/10.1016/j.exis.2021.100991", + "abstract": "Natural resources can become a catalyst for growth and development in resource-abundant developing countries if they are governed in a transparent and accountable manner. Despite the popularization of transparency and accountability in academic and public policy discussions, we know little about how they become part of daily interactions. This article critically analyses how and what kind of transparency and accountability is discursively enacted by community actors in politically unstable settings with weak institutions such as Kyrgyzstan. The case study shows the extension of the transparency and accountability agenda from right-to-know and environmental-financial reporting to direct engagement and livelihood improvement. The analysis suggests that political-economic opportunities and youth-led community activism define the local approach to transparency and accountability. This article calls for studying transparency and accountability beyond formal institutionalism and top-down (elite) politics.", + "citation_count": 15, + "topics": [ + "International Development and Aid", + "Mining and Resource Management", + "Natural Resources and Economic Development" + ], + "discovered_via": [ + "neighbor:openalex:W4400303336" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2495360771", + "title": "Growth and Structure of Workforce in India: An Analysis of Census 2011 Data", + "year": 2013, + "url": "https://openalex.org/W2495360771", + "doi": null, + "abstract": "Census 2011 brings new dimension to ongoing debate on the decline in the growth of employment from the last two decade. The census 2011 result gives better picture when compared with NSSO estimation of workforce. It is observed that there is a fast decelerating rate of growth in overall workforce, particularly that of females, between 2001 and 2011. But the work participation rate has not declined, if not increase, as the rate of growth in workforce is not less than that of population. Secondly, incremental workforce especially the male is getting reduced to marginal workers category whereas the high concentration of female in the category of marginal workers is slightly reduced. Occupational distribution of workforce shows that cultivators are declining such decline in agriculture is replaced by increasing agricultural labour. Growth of workforce in non-agriculture is higher than that of agriculture. Growth of female workers engaged in non-agriculture is higher than their male counterparts.", + "citation_count": 15, + "topics": [ + "Indian Economic and Social Development", + "Agricultural risk and resilience", + "Global Health and Epidemiology" + ], + "discovered_via": [ + "neighbor:openalex:W4402876502", + "neighbor:openalex:W4407192387" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2134894512", + "title": "The Information Used to Judge Supportiveness Depends on Whether the Judgment Reflects the Personality of Perceivers, the Objective Characteristics of Targets, or their Unique Relationships", + "year": 2004, + "url": "https://doi.org/10.1521/jscp.23.6.817.54806", + "doi": "https://doi.org/10.1521/jscp.23.6.817.54806", + "abstract": "People who judge their relationships as more supportive enjoy better mental health than people who judge their relationships more negatively. We investigated how people made these judgments; specifically, how people weighed different types of information about targets under three different conditions: when judgments reflected the personality of perceivers, the objective characteristics of targets, and the unique relationships between perceivers and targets. Participants (i.e., perceivers) judged the same four videotaped targets on personality, similarity to perceivers and likely supportiveness. As in previous research, perceivers based their judgments on perceived target similarity to perceivers, and on target personality. However, how perceivers weighed personality and similarity information varied dramatically depending upon whether the judgment reflected the personality of perceivers, the objective characteristics of targets, or the relationship between perceivers and targets. Implications for understanding how people make support judgments were discussed.", + "citation_count": 15, + "topics": [ + "Attachment and Relationship Dynamics", + "Social and Intergroup Psychology", + "Behavioral Health and Interventions" + ], + "discovered_via": [ + "neighbor:openalex:W4394590365", + "neighbor:openalex:W4404228668" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4407762039", + "title": "Listen for a change? A longitudinal field experiment on listening\u2019s potential to enhance persuasion", + "year": 2025, + "url": "https://doi.org/10.1073/pnas.2421982122", + "doi": "https://doi.org/10.1073/pnas.2421982122", + "abstract": "Scholars and practitioners widely posit that listening to other people enhances efforts to persuade them. Listening may enhance persuasion by promoting cognitive processing, reducing defensiveness, and improving perceptions of the persuader. However, empirical tests of this widely theorized hypothesis are surprisingly scarce. We review the case for and against this hypothesis, arguing previous research has not sufficiently attended to reasons why listening may not enhance persuasion. We test this hypothesis using a preregistered, well-powered field experiment in which trained professional canvassers, acting as confederates, had \u223c10 min video conversations with U.S. participants ( N = 1,485) about unauthorized immigration, a salient topic of disagreement. We independently randomized whether confederates shared a persuasive narrative about an undocumented immigrant and whether they practiced high-quality nonjudgmental listening to participants\u2019 opinions. We measured outcomes immediately after the conversation and again five weeks later. Sharing a persuasive narrative meaningfully and durably reduced prejudice and changed policy attitudes. The listening manipulation also successfully improved perceptions of the persuader and increased processing. Surprisingly, however, the listening manipulation did not enhance persuasion: Sharing a persuasive narrative was just as effective in the absence of high-quality listening. We discuss theoretical and practical implications.", + "citation_count": 14, + "topics": [ + "Communication in Education and Healthcare", + "Media Influence and Health", + "Language, Discourse, Communication Strategies" + ], + "discovered_via": [ + "query:AI hiring methods causal evaluation field experiment longitudinal quality of hire" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4200071106", + "title": "The Concept of Originality in Academic Research of Engineering", + "year": 2021, + "url": "https://doi.org/10.1155/2021/9462201", + "doi": "https://doi.org/10.1155/2021/9462201", + "abstract": "The definition of research appears to be a controversial subject in the academic community. It is almost becoming apparent that research remains a key element in master\u2019s and doctoral degrees. Originality is, to varying degrees, the primary concern for the genuineness of research, but there is a difference of opinion on the concept of originality. In practice, a subjective analysis of originality is performed at the time of the assessment of the said degrees\u2019 theses; therefore, the resulting evaluation is affected by the difference of opinion on the concept of originality. It has also emerged that the concept of originality in research is also marginally unique in various areas. This study is focused upon reaching a point of agreement for the definition of originality in the theses/dissertation of masters and doctorate degrees in engineering only. The outcome of the research should be something new and originally contribute to the body of knowledge. This study focused on to arrive at an accurate definition of originality in university degrees in the field of engineering. An online survey was designed and carried out on the basis of evidence and expert opinion. The survey was distributed amongst engineering peers. The results of the survey are systematically summed up in the study.", + "citation_count": 14, + "topics": [ + "Engineering Education and Curriculum Development", + "Evaluation of Teaching Practices" + ], + "discovered_via": [ + "neighbor:openalex:W4381684347" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1822168192", + "title": "The Role of Job Relatedness and Self\u2010efficacy in Applicant Perceptions of Fairness in a High\u2010stakes Selection Setting", + "year": 2015, + "url": "https://doi.org/10.1111/ijsa.12118", + "doi": "https://doi.org/10.1111/ijsa.12118", + "abstract": "This article presents results from two samples of applicants (total N = 368) for general practitioner posts in the United Kingdom. The roles of job relatedness and self\u2010efficacy in fairness perceptions were explored, with data gathered at two time points: immediately after testing and one month later following outcome (pass/fail) feedback. Overall, results indicated that in two samples, job relatedness perceptions measured at the time of testing predicted fairness perceptions measured following outcome feedback. In addition, the stage in the selection process (shortlisting vs. assessment center) was important in determining the extent to which job relatedness perceptions predicted fairness. Findings also suggest that self\u2010efficacy may be a predictor, rather than an outcome variable, in applicant fairness perceptions in this high\u2010stakes setting. Results are discussed in relation to their practical and theoretical implications.", + "citation_count": 14, + "topics": [ + "Diversity and Career in Medicine", + "Employer Branding and e-HRM", + "Medical Education and Admissions" + ], + "discovered_via": [ + "neighbor:openalex:W4394590365" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2077865380", + "title": "Amount of altruistic punishment accounts for subsequent emotional gratification in participants with primary psychopathy", + "year": 2011, + "url": "https://doi.org/10.1016/j.paid.2011.07.006", + "doi": "https://doi.org/10.1016/j.paid.2011.07.006", + "abstract": "", + "citation_count": 14, + "topics": [ + "Psychopathy, Forensic Psychiatry, Sexual Offending", + "Deception detection and forensic psychology", + "Psychology of Moral and Emotional Judgment" + ], + "discovered_via": [ + "neighbor:openalex:W4394590365", + "neighbor:openalex:W4404228668" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2415869793", + "title": "[Summary of the practice guideline for shoulder complaints from the Dutch College of General Practitioners].", + "year": 2009, + "url": "https://pubmed.ncbi.nlm.nih.gov/19818179", + "doi": null, + "abstract": "The second revision of the practice guideline for shoulder complaints was presented in 2008 by the Dutch College of General Practitioners. This guideline provides a standard for the diagnosis and treatment of shoulder complaints by general practitioners. The most important items in this new guideline are reviewed.", + "citation_count": 14, + "topics": [ + "Shoulder Injury and Treatment", + "Clinical practice guidelines implementation", + "Musculoskeletal Disorders and Rehabilitation" + ], + "discovered_via": [ + "neighbor:openalex:W4407173730" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2022519358", + "title": "Reflections On Privacy 2.O", + "year": 2008, + "url": "https://doi.org/10.1038/scientificamerican0908-50", + "doi": "https://doi.org/10.1038/scientificamerican0908-50", + "abstract": "", + "citation_count": 14, + "topics": [ + "Privacy, Security, and Data Protection" + ], + "discovered_via": [ + "neighbor:openalex:W4318407310" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4382930947", + "title": "Agent transparency in mixed-initiative multi-UxV control: How should intelligent agent collaborators speak their minds?", + "year": 2023, + "url": "https://doi.org/10.1016/j.chb.2023.107866", + "doi": "https://doi.org/10.1016/j.chb.2023.107866", + "abstract": "", + "citation_count": 13, + "topics": [ + "Smart Grid Security and Resilience", + "Reinforcement Learning in Robotics", + "Smart Grid Energy Management" + ], + "discovered_via": [ + "neighbor:openalex:W4386714740" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3163373470", + "title": "Can people detect the trustworthiness of strangers based on their facial appearance?", + "year": 2020, + "url": "https://doi.org/10.31234/osf.io/ayqeh", + "doi": "https://doi.org/10.31234/osf.io/ayqeh", + "abstract": "Although cooperation can lead to mutually beneficial outcomes, cooperative actions only pay off for the individual if others can be trusted to cooperate as well. Identifying trustworthy interaction partners is therefore a central challenge in human social life. How do people navigate this challenge? Prior work suggests that people rely on the facial appearance of strangers to judge their trustworthiness. However, the question of whether these judgments are actually accurate remains debated. The present research examines accuracy in trustworthiness detection from faces and three moderators proposed by previous research. We investigate whether people show above-chance accuracy (a) when they make trust decisions vs. provide explicit trustworthiness ratings, (b) when judging male vs. female counterparts, and (c) when rating cropped images (with non-facial features removed) vs. uncropped images. Results of two studies showed that incentivized trust decisions (Study 1, n = 131 university students) and incentivized predictions of counterparts\u2019 trustworthiness (Study 2, n = 266 university students) were unrelated to the actual trustworthiness of counterparts. Moreover, accuracy was not moderated by stimulus type (cropped vs. uncropped faces) or counterparts\u2019 gender. Overall, these findings suggest that people are unable to detect the trustworthiness of strangers based on their facial appearance, when this is the only information available to them.", + "citation_count": 13, + "topics": [ + "Psychology of Moral and Emotional Judgment", + "Death Anxiety and Social Exclusion", + "Cultural Differences and Values" + ], + "discovered_via": [ + "neighbor:openalex:W4381848566" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2584455473", + "title": "Research on Methods for Improving Robustness of Cascading Failures of Interdependent Networks", + "year": 2017, + "url": "https://doi.org/10.1007/s11277-017-4041-6", + "doi": "https://doi.org/10.1007/s11277-017-4041-6", + "abstract": "", + "citation_count": 13, + "topics": [ + "Complex Network Analysis Techniques", + "Opinion Dynamics and Social Influence", + "Network Security and Intrusion Detection" + ], + "discovered_via": [ + "neighbor:openalex:W4318407310" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1962711591", + "title": "The Past and Future Evolution of the Dental Workforce Team", + "year": 2012, + "url": "https://doi.org/10.1002/j.0022-0337.2012.76.8.tb05355.x", + "doi": "https://doi.org/10.1002/j.0022-0337.2012.76.8.tb05355.x", + "abstract": "This article looks at changes in the number and mix of providers in the dental workforce over the past sixty years. First, enrollment trends in dental education programs are investigated. These educational programs feed directly into the dental workforce. Then, the changes in the dental workforce are examined. The focus of this investigation is the composition of the dental workforce and how the components of the workforce have changed over time. The forces that are responsible for these changes in the workforce are explored next. Finally, the possibility for workforce changes in the future is considered.", + "citation_count": 13, + "topics": [ + "Dental Education, Practice, Research", + "Medical Education and Admissions", + "Global Health Workforce Issues" + ], + "discovered_via": [ + "neighbor:openalex:W4402876502", + "neighbor:openalex:W4407192387" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4255216743", + "title": "\"Green Multi Business Models\u201d How to Measure Green Business Models and Green Business Model Innovation?", + "year": 2021, + "url": "https://doi.org/10.1007/s11277-021-09189-2", + "doi": "https://doi.org/10.1007/s11277-021-09189-2", + "abstract": "", + "citation_count": 12, + "topics": [ + "Sustainable Supply Chain Management", + "Environmental Sustainability in Business", + "Sustainable Industrial Ecology" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2913177154", + "title": "Regional contrasting DTR\u2019s predictability over China", + "year": 2019, + "url": "https://doi.org/10.1016/j.physa.2019.01.077", + "doi": "https://doi.org/10.1016/j.physa.2019.01.077", + "abstract": "", + "citation_count": 12, + "topics": [ + "Complex Systems and Time Series Analysis", + "Climate variability and models", + "Plant Water Relations and Carbon Dynamics" + ], + "discovered_via": [ + "neighbor:openalex:W4386142022" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1536485080", + "title": "Temas emergentes em gest\u00e3o de pessoas: uma an\u00e1lise da produ\u00e7\u00e3o acad\u00eamica", + "year": 2011, + "url": "https://doi.org/10.5902/198346592401", + "doi": "https://doi.org/10.5902/198346592401", + "abstract": "This study shows outcomes from a research about national and international academic production about eight emergent human resource themes: strategic human resource management, diversity management, talent management, international human resource management, organizational learning, social responsibility, generation management, and kinds of flexible job. This group of themes were defined by a previous bibliography revision. This revision included studies and researches published at the last five years about human resource trends at important journals and events. The outcomes were combined with specialist opinions. The analysis identified that international human resource management, strategic human resource management, organizational learning, diversity management were the most common themes. The study also identified that under the methological point of view, the bigger part of the researches were exploratory; the most common methological studies profile were theoretical and empirical and the quantitative approach was predominant.", + "citation_count": 12, + "topics": [ + "Business and Management Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1607297154", + "title": "Do Lunch or Be Lunch: The Power of Predictability in Creating Your Future", + "year": 1997, + "url": "http://ci.nii.ac.jp/ncid/BA33148962", + "doi": null, + "abstract": "Preface Introduction 1. Predictability: The Good, the Bad, and the Ugly 2. Predictability Important? Says Who? 3. Where Prediction and Predictability Live 4. Variety versus Predictability 5. Predictability in the Past 6. How Hard Is Prediction? 7. Acting to Make the Future Happen 8. You, Me, and Agreement 9. The Technology of Agreement 10. Society and Unpredictability 11. Lone Wolves and Team Players 12. The Question Acknowledgments Notes Select Bibliography Index About the Authors", + "citation_count": 12, + "topics": [ + "Complex Systems and Decision Making" + ], + "discovered_via": [ + "neighbor:openalex:W4386142022" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2037749514", + "title": "Race and socioeconomic status as confounding variables in the accurate diagnosis of alcoholism", + "year": 1982, + "url": "https://doi.org/10.1002/1097-4679(198207)38:3<665::aid-jclp2270380338>3.0.co;2-r", + "doi": "https://doi.org/10.1002/1097-4679(198207)38:3<665::aid-jclp2270380338>3.0.co;2-r", + "abstract": "Utilized two psychologists as actors in making four videotapes to depict background information and verbal modes of communication for alcoholics according to black-white race differences and high-low socioeconomic status. Forty graduate psychology students (10 per group) then were shown randomly one of the four recordings and asked to make a diagnosis. A significant difference was found for the effects of race, socioeconomic status, and race X socioeconomic status interaction on the correct diagnosis of alcoholism. Results were interpreted as suggesting that a clinician's personal biases and/or prejudices might extend into the clinical diagnosis and treatment of clients.", + "citation_count": 12, + "topics": [ + "Substance Abuse Treatment and Outcomes", + "Child and Adolescent Psychosocial and Emotional Development", + "Racial and Ethnic Identity Research" + ], + "discovered_via": [ + "neighbor:openalex:W4399518864" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4283798182", + "title": "Business Models and Sustainable Development Goals", + "year": 2022, + "url": "https://doi.org/10.3390/books978-3-0365-4306-2", + "doi": "https://doi.org/10.3390/books978-3-0365-4306-2", + "abstract": "This Special Issue on “Business Models and Sustainable Development Goals (SDG)” presents five research studies that examine transformative business models designed to support the United Nations SDG Agenda for 2030. The studies examine SDGs from the firm to national levels. Every organization has a business model that defines how the organization is designed to function. It is the engine that powers an organization, defining the value proposition of the venture, how it balances resources with the ecosystem where it operates, and how it generates cash flow and creates value. Changes to an organization’s business model are recognized as a fundamental approach to implementing innovations for sustainability. The capability to transform or transition to new business models is an important source of competitive advantage, providing leverage to improve the performance of an organization. A sustainable business model includes pro-active management, monetary and non-monetary value for a broad range of stakeholders, and takes a long-term perspective. A sustainable business model is where change, success, and hope for our planet’s future rests.", + "citation_count": 11, + "topics": [ + "Sustainable Development and Environmental Policy", + "Innovation and Socioeconomic Development", + "Sustainable Finance and Green Bonds" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3000619383", + "title": "In or Out? Perceptions of Inclusion and Exclusion among AIS Members", + "year": 2020, + "url": "https://doi.org/10.17705/1cais.04605", + "doi": "https://doi.org/10.17705/1cais.04605", + "abstract": "People want a sense of community, a benefit that a professional association such as the Association for Information Systems (AIS) can provide to members. When attempts to create a shared experience fall short and we feel excluded, we disengage and stop further attempts to participate. In this paper, we lay a foundation for individual and association inclusion practices in the AIS. First, we describe the current state of inclusion practices in the academy and in the AIS. Then, we describe findings from a survey of AIS members that measured their perceptions about inclusion and exclusion and factors that cultivated these perceptions. In doing so, we establish a baseline against which we can measure future change. Our data yields key insights about diversity and inclusion in the AIS, and we offer recommendations for all individuals in various roles and positions in the AIS.", + "citation_count": 11, + "topics": [ + "Knowledge Management and Sharing", + "Technology Adoption and User Behaviour", + "Social Media and Politics" + ], + "discovered_via": [ + "neighbor:openalex:W4366989571" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4246571544", + "title": "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track", + "year": 2020, + "url": "https://doi.org/10.18653/v1/2020.coling-industry", + "doi": "https://doi.org/10.18653/v1/2020.coling-industry", + "abstract": "2020) Industry Track.This year marks the first in which COLING has a dedicated track for research related to computational linguistics deployed in real-world settings.In recent years, industrial research has been increasingly influential in the field of computational linguistics-both in the form of research departments that contribute to the advancement of computational linguistics, and also by way of the knowledgeable inventors and developers of innovative language and speech products.The goals of this session are to foster connections between industry practitioners, share insights from industry research to the broader community, and increase engagement with academia on research questions of high priority in industry.This session will showcase commercially-driven research from diverse angles, including the challenges of doing applied research at scale, production scalability, and a shifting data landscape.We received 124 submissions (79 long and 45 short) and had an acceptance rate of 23%. 1 Based on the first author's affiliation, an estimated 76% of submissions came from industry and 24% from academia.Geographically, most submissions were from North America (44%), 27-28% from Europe and Asia (respectively), and 1% from Africa.", + "citation_count": 11, + "topics": [ + "Semantic Web and Ontologies" + ], + "discovered_via": [ + "neighbor:openalex:W4392871535" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2075359942", + "title": "Citizens' E-Government Services Adoption", + "year": 2014, + "url": "https://doi.org/10.4018/ijpada.2014040105", + "doi": "https://doi.org/10.4018/ijpada.2014040105", + "abstract": "This study investigates the factors that influencing citizens' intention to use e-government services and its causal relationships using the Unified Theory of Acceptance and Use of Technology model (UTAUT). Data relating to the constructs were collected from 150 respondents but only 85 responses were received and subjected to Structural Equation Modeling analysis (SEM). The proposed model fits the data well. The findings reveal that facilitating condition, social influence, attitude, effort expectancy and performance expectancy determine citizens' intention towards e-government services. Additionally, attitude is predicted jointly by effort expectancy and performance expectancy. This study would help government policy decision makers to increase the adoption of e-government services in Tunisia country.", + "citation_count": 11, + "topics": [ + "Technology Adoption and User Behaviour", + "E-Government and Public Services", + "Digital Marketing and Social Media" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2093873545", + "title": "An evaluation of the written selection test: effectiveness and alternatives", + "year": 1998, + "url": "https://doi.org/10.1016/s0047-2352(97)00084-6", + "doi": "https://doi.org/10.1016/s0047-2352(97)00084-6", + "abstract": "", + "citation_count": 11, + "topics": [ + "Medical Education and Admissions", + "Policing Practices and Perceptions", + "Social and Intergroup Psychology" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4392871535", + "title": "TRANSFORMATION OF RECRUITMENT PROCESS THROUGH IMPLEMENTATION OF AI SOLUTIONS", + "year": 2024, + "url": "https://doi.org/10.55640/jme-04-02-03", + "doi": "https://doi.org/10.55640/jme-04-02-03", + "abstract": "This article reviews how the recruitment process across various industries has been transformed due to recent advancements in artificial intelligence (AI) solutions. A literature review summarizes key findings on AI-enabled improvements in sourcing, screening, assessment, interviewing, and onboarding of candidates. Both recruitment analytics using big data and conversational AI chatbots are discussed as drivers reshaping traditional human resource workflows. Challenges around AI bias, privacy, and job automation fears are also analyzed. Overall, AI implementation led to enhanced efficiency, quality, and experience in talent acquisition. However, maintaining a human touch remains vital, suggesting an optimal balance between AI and human recruiters.", + "citation_count": 10, + "topics": [ + "AI and HR Technologies", + "Big Data and Business Intelligence", + "Employer Branding and e-HRM" + ], + "discovered_via": [ + "query:AI recruiting business value time to hire cost per hire quality of hire recruiter productivity ROI" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4402876502", + "title": "Strategic Innovation in HRIS and AI for Enhancing Workforce Productivity in SMEs: A Systematic Review", + "year": 2024, + "url": "https://doi.org/10.20944/preprints202409.1996.v1", + "doi": "https://doi.org/10.20944/preprints202409.1996.v1", + "abstract": "This systematic review critically examines the adoption and integration of Human Resource Information Systems (HRIS) and Artificial Intelligence (AI) in small and medium-sized enterprises (SMEs), with a focus on enhancing workforce productivity and strategic decision-making. Using the PRISMA framework, 100 research articles from reputable sources such as Google Scholar, Scopus, and Web of Science were analyzed. The analysis reveals that HRIS adoption can improve employee productivity by 29%, decision-making by 20%, and operational efficiency by 26%, highlighting its transformative impact on SMEs. The review identifies major challenges, including high implementation costs, limited IT resources, and integration difficulties with AI and machine learning technologies. Despite these barriers, integrating AI into HRIS presents significant opportunities for SMEs, fostering innovation in talent management, compliance automation, and data-driven decision-making, thus creating a competitive edge in rapidly evolving markets. Actionable insights for practitioners emphasize the need for cost-effective, scalable HRIS solutions tailored to the unique operational needs of SMEs, while researchers are urged to further explore AI-driven HRIS advancements to address current gaps in workforce engagement and performance management. This review offers a comprehensive roadmap for future HRIS innovations and underscores the strategic importance of digital transformation in human resources for sustained SME competitiveness.", + "citation_count": 10, + "topics": [ + "AI and HR Technologies", + "Employer Branding and e-HRM", + "Digital Transformation in Industry" + ], + "discovered_via": [ + "query:AI recruiting business value time to hire cost per hire quality of hire recruiter productivity ROI" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2738656338", + "title": "Analysing Corruption: Effects on the Transparency of Public Administrations", + "year": 2017, + "url": "https://doi.org/10.1007/978-3-319-57018-1_13", + "doi": "https://doi.org/10.1007/978-3-319-57018-1_13", + "abstract": "", + "citation_count": 10, + "topics": [ + "Corruption and Economic Development", + "Auction Theory and Applications", + "Italy: Economic History and Contemporary Issues" + ], + "discovered_via": [ + "neighbor:openalex:W4386714740", + "neighbor:openalex:W4404254054" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4391913857", + "title": "ESKVS: efficient and secure approach for keyframes-based video summarization framework", + "year": 2024, + "url": "https://doi.org/10.1007/s11042-024-18405-7", + "doi": "https://doi.org/10.1007/s11042-024-18405-7", + "abstract": "", + "citation_count": 9, + "topics": [ + "Video Analysis and Summarization", + "Music and Audio Processing", + "Multimedia Communication and Technology" + ], + "discovered_via": [ + "neighbor:openalex:W4392172846" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2034952755", + "title": "Perceived and objective usability and visual appeal in a website domain with a less developed mental model", + "year": 2014, + "url": "https://doi.org/10.1145/2686612.2686660", + "doi": "https://doi.org/10.1145/2686612.2686660", + "abstract": "In Human-Computer Interaction (HCI), the usability and aesthetics of webpages have been studied extensively. However, research into participant experience and expertise with the website domain being studied is often overlooked. This paper strived to acquire an understanding of the relationship between usability and visual appeal by examining it in a domain that had less developed mental models, to exclude the influence of past experiences. Two studies were conducted to (1) find a domain with a less developed mental model and (2) examine correlations between usability and aesthetics in the less familiar domain. Results of Study 1 showed that mental models were weaker for city council websites. Results of Study 2 showed that, for city council websites, pre- and post-use perceived usability and visual appeal, perceived usability and the number of passed tasks, and average number of hovers (objective usability) and visual appeal were all significantly and positively correlated.", + "citation_count": 9, + "topics": [ + "Innovative Human-Technology Interaction", + "Color perception and design", + "Digital Marketing and Social Media" + ], + "discovered_via": [ + "neighbor:openalex:W4394591980" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1521483232", + "title": "Cognitive Tests, Constructs, and Content Validity: A commentary on Schmidt ()", + "year": 2012, + "url": "https://doi.org/10.1111/j.1468-2389.2012.00576.x", + "doi": "https://doi.org/10.1111/j.1468-2389.2012.00576.x", + "abstract": "This commentary discusses a number of issues that build on S chmidt's ( International Journal of Selection and Assessment , 20, 1\u201013 (2012)) perspective on content validity and cognitive tests. First, it elaborates on the relationship between the treatment of content validity in various professional standards and government guidelines. Second, it offers a differing perspective on the definition of \u2018construct\u2019 than that taken by S chmidt. Third, it elaborates on the settings in which content validity can and cannot be used to support the use of a given test in the cognitive ability domain.", + "citation_count": 9, + "topics": [ + "Psychometric Methodologies and Testing", + "Educational and Psychological Assessments", + "Student Assessment and Feedback" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2411338097", + "title": "[Physical activity and leisure time in children. I: Relation to socioeconomic status].", + "year": 1997, + "url": "https://pubmed.ncbi.nlm.nih.gov/9157798", + "doi": null, + "abstract": "OBJECTIVE: To assess physical activity and leisure time activities in relation to socioeconomic status. PATIENTS AND METHODS: We have studied 1,078 boys and girls from 24 schools from areas of different geographical and socioeconomical status. Physical activity and leisure time activities were assessed by a questionnaire. RESULTS: We have observed that adolescents spend more time watching T.V. than doing physical activity (p = 0.0001). In general, boys prefer strong physical activity. Children from the lowest socioeconomic families did less physical activity than children from the highest socioeconomic families (p < or = 0.05). In girls, those with the lowest socioeconomic status spent the greatest amount of time watching T.V. (p = 0.0001). CONCLUSIONS: From our results, we deduce that we must improve physical activity habits in children, especially in those from the lowest socioeconomic families.", + "citation_count": 9, + "topics": [], + "discovered_via": [ + "neighbor:openalex:W4399518864" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4385368139", + "title": "Familiar transformative experiences", + "year": 2023, + "url": "https://doi.org/10.1007/s11229-023-04275-3", + "doi": "https://doi.org/10.1007/s11229-023-04275-3", + "abstract": "Abstract On the standard Paulian definition of epistemically transformative experiences (ETE), we can\u2019t know what an ETE is like before we have it. ETEs are new kinds of experiences and, importantly, can\u2019t be imagined\u2014this is why they have a unique ability to teach us what a particular experience is like. Contra Paul, some philosophers (Sharadin, 2015; Wilkenfeld, 2016; Ismael, 2019; Kind, 2020; Daoust, 2021; Cath, 2022) have argued that transformative experiences can be imagined. A neglected consequence of this argument is that if transformative experiences can in fact be imagined, then it is unclear how they could be epistemically transformative. What do they teach us if we can imagine what they\u2019re like in advance? I will argue not only that imaginable experiences can be transformative, but that experiences of a kind which an agent is experientially acquainted with can also be transformative. This latter kind of transformative experiences, which I will call familiar transformative experiences, are transformative not because the agent learns what a new kind of experience is like\u2014by definition, they are not new kinds of experiences\u2014but because the transformativeness of the experience is brought out by features of the agent experiencing them. Epistemic transformation in these cases may be explained by facts about the agent\u2019s perspective and social environment, which allow them to appreciate elements of the experience they did not previously.", + "citation_count": 8, + "topics": [ + "Philosophical Ethics and Theory", + "Free Will and Agency", + "Psychology of Moral and Emotional Judgment" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4390718435", + "title": "Analysis of Professors\u2019 Experiences with Generative AI and the Concerns of Classroom Use : Application of the Concerns-Based Adoption Model (CBAM)", + "year": 2023, + "url": "https://doi.org/10.46392/kjge.2023.17.6.333", + "doi": "https://doi.org/10.46392/kjge.2023.17.6.333", + "abstract": "The rise of generative AI, such as ChatGPT, poses challenges for the education sector. To explore strategies for addressing generative AI issues and identifying educational applications, it is essential for us to understand the concerns and attitudes of educators, who are the key implementers in education. In this context, this study investigated the experiences and concerns of university professors regarding the educational use of generative AI by utilizing the Concerns-Based Adoption Model (CBAM). Data was collected from 100 professors representing various disciplines at University A in Gyeonggi-do. The majority of respondents had some experience with generative AI, but its educational utilization was limited. Concerns included the provision of incorrect or biased information, ethical issues, and users' lack of ability to use generative AI. However, 61% of respondents expressed their intention to apply generative AI in their courses in the upcoming semester. Concern levels aligned with the non-user profile, with lower concerns in the consequence stage. There were no significant differences in concern levels based on the professors' disciplines, but differences were observed based on their experience and intention to use generative AI in their classes. Professors who had already used generative AI in their classes showed higher concerns regarding the consequence, collaboration, and refocusing stages. Based on these findings, implications for the educational use of generative AI in higher education were discussed.", + "citation_count": 8, + "topics": [ + "Education and Learning Interventions" + ], + "discovered_via": [ + "neighbor:openalex:W4390315357", + "neighbor:openalex:W4391830676" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3032998312", + "title": "Obstacles of Mobile Crowdsourcing: A Survey", + "year": 2019, + "url": "https://doi.org/10.1109/punecon46936.2019.9105827", + "doi": "https://doi.org/10.1109/punecon46936.2019.9105827", + "abstract": "Mobile Crowdsourcing is now a highly compelling topic for research. Using crowdsourcing, a user can request for a solution to any problem he or she is facing to the mass people. In today's world, crowdsourcing can be applied to solve industrial, academical, business, and other problems. The utility of the crowdsourcing system depends on the involvement of the people in the task. However, due to several reasons, people become unwilling to participate in crowdsourcing tasks. In this paper, we have identified the reasons that prevent users from engaging in the crowdsourcing task. The obstacles are later classified according to the types of participants of a crowdsourcing task. Each of the participants has several obstacles that demotivate them from participating in the crowdsourcing. This survey paper will support us to understand the challenges that should be faced when designing a crowdsourced framework. If we can address these challenges, it will be easier to build a crowdsourcing framework to solve any problem.", + "citation_count": 8, + "topics": [ + "Mobile Crowdsensing and Crowdsourcing", + "Data Stream Mining Techniques", + "Personal Information Management and User Behavior" + ], + "discovered_via": [ + "neighbor:openalex:W4401533174" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1986945951", + "title": "The Study of Enterprise Human Resource Evaluation Research Based on Neural Network", + "year": 2010, + "url": "https://doi.org/10.1109/isip.2010.13", + "doi": "https://doi.org/10.1109/isip.2010.13", + "abstract": "In order to satisfy the needs of human resources management and development, this study took R&D professionals as the research object and proposed an evaluation model for high-tech enterprise human resources based on artificial neural network, then trained and tested the neutral network for personnel evaluation. And the network was improved to be very effective to stimulate the evaluation of human resource. At last, it explained the realistic significance of this HR evaluation.", + "citation_count": 8, + "topics": [ + "Industrial Technology and Control Systems" + ], + "discovered_via": [ + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4396701345", + "title": "STKVS: secure technique for keyframes-based video summarization model", + "year": 2024, + "url": "https://doi.org/10.1007/s11042-024-18909-2", + "doi": "https://doi.org/10.1007/s11042-024-18909-2", + "abstract": "", + "citation_count": 7, + "topics": [ + "Video Analysis and Summarization", + "Music and Audio Processing", + "Advanced Steganography and Watermarking Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4392172846", + "neighbor:openalex:W4394591980" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4383031710", + "title": "Impact of Generative AI on IT Professionals", + "year": 2023, + "url": "https://doi.org/10.22214/ijraset.2023.54515", + "doi": "https://doi.org/10.22214/ijraset.2023.54515", + "abstract": "Abstract: This research paper aims to explore the impact of generative artificial intelligence (AI) on IT professionals. Generative AI refers to the technology that can autonomously create new content, such as images, text, and even code. With the rapid advancements in AI, the role of IT professionals is evolving, and it is crucial to understand the implications of generative AI on their work. This paper examines the potential benefits, challenges, and ethical considerations associated with the integration of generative AI in the IT industry. Through a comprehensive analysis of existing literature, this paper sheds light on the transformation of IT professionals' responsibilities, skills, and career prospects in the era of generative AI.", + "citation_count": 7, + "topics": [ + "Artificial Intelligence in Healthcare and Education" + ], + "discovered_via": [ + "neighbor:openalex:W4391830676" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3120463682", + "title": "Digital transformation and financial performance", + "year": 2021, + "url": "https://doi.org/10.4324/9781003008637-25", + "doi": "https://doi.org/10.4324/9781003008637-25", + "abstract": "This study aims to assess the impact of digital transformation on financial performance. Conceptualizing digital transformation as a measure for digital business model change, we explore how the relationship between firms\u2019 digital transformation and financial performance depends on the presence of dedicated digital experts: digital specialists. Using survey data of 189 Dutch SMEs, we find that the presence of digital specialists does not directly improve financial performance, but that it helps to convert digital transformation more effectively: the positive association between digital transformation and financial performance only exists when firms have a digital specialist. Our contingency approach underlines the context-specific nature of digital transformation and highlights the importance of specialists in unlocking the value of digital transformation. Theoretical and managerial implications are discussed.", + "citation_count": 7, + "topics": [ + "Innovation Diffusion and Forecasting", + "Digital Platforms and Economics", + "Blockchain Technology Applications and Security" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3203988261", + "title": "Evaluation of one of leading Indonesia\u2019s digital wallet using the unified theory of acceptance and use of technology", + "year": 2021, + "url": "https://doi.org/10.11591/ijeecs.v24.i2.pp1036-1046", + "doi": "https://doi.org/10.11591/ijeecs.v24.i2.pp1036-1046", + "abstract": "The goal of this research is to assess the utilization of one of Indonesia's most popular digital wallets. Respondents in this study range in age from 15 to 44 years old and live on Java Island. The author employs the unified theory of acceptance and utilization of technology (UTAUT) paradigm to assess the use of this digital wallet. This UTAUT model comprises four latent variables that affect behavioral intention (BI) and use behavior (UB): performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). In this study, the UTAUT model is combined with two additional variables: perceived risk (PR) and promotional benefits (PB). According to the findings, performance expectancy, social influence, and perceived risk all have an impact on behavioral intention, whereas effort expectancy has a less impact. Furthermore, the factors facilitating conditions and promotional benefits have a minor impact on use behavior, whereas behavioral intention factors have a considerable impact on use behavior.", + "citation_count": 7, + "topics": [ + "Technology Adoption and User Behaviour", + "Digital Marketing and Social Media", + "Financial Literacy and Behavior" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4246804435", + "title": "ARTIFICIAL INTELLIGENT RECRUITMENT SYSTEM", + "year": 2020, + "url": "https://doi.org/10.33564/ijeast.2020.v05i03.037", + "doi": "https://doi.org/10.33564/ijeast.2020.v05i03.037", + "abstract": "Artificial Intelligence (AI) technology has revolutionized every aspect of personal as well as professional life by making everything smarter. AI for selection of suitable candidates is a developing category of HR automation devised to curtail or even abolish time engrossing tasks like manually screening resumes. The artificial intelligent recruitment system is devised to automate few tasks of the recruiting work flow, especially repetitive, high-volume tasks. Due to artificial intelligence in recruitment, data becomes easier to collect, access, and analyze. The advantage of adopting AI in the recruitment system is its capability to make use of data to institutionalize the practicality between the candidates experience, expertise, ability, competence and the requirements of the job. Another appealing benefit of AI in recruitment, still in the sourcing field can be found in the candidate matching part of the process. AI technology can be used to enhance the prospect experience, that is to exactly figure out what position and type of content your prospects are engrossed in. Among other things, NLP and AI can help on resume classification, ranking, deep extraction, identification and semi-automation in the recruitment process.", + "citation_count": 7, + "topics": [ + "Knowledge Management and Technology" + ], + "discovered_via": [ + "neighbor:openalex:W4404228668" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2959418760", + "title": "Technology Acceptance Model on Micro Business Owner in Grogol Petamburan District", + "year": 2019, + "url": "https://doi.org/10.1145/3335484.3335498", + "doi": "https://doi.org/10.1145/3335484.3335498", + "abstract": "This research is quantitative research that aims to determine the factors that influence the acceptance of information technology among micro-entrepreneurs. This research is based on the Unified Theory of Acceptance and Use of Technology (UTAUT). The object of this research is micro business people in Grogol Petamburan district. The results of the study state that performance expectancy and effort expectancy influence behavioral intention, while social influence does not have a significant effect on behavioral intention and facilitating conditions that have no significant effect on use behavior. Behavioral intention affects use behavior. In addition, gender differences reinforce the influence of performance expectancy and effort expectancy on behavioral intention, but do not strengthen the influence of social influence to behavioral intention", + "citation_count": 7, + "topics": [ + "Technology Adoption and User Behaviour", + "Financial Literacy and Behavior", + "SMEs Development and Digital Marketing" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2573606541", + "title": "Biologically-inspired object recognition system for recognizing natural scene categories", + "year": 2016, + "url": "https://doi.org/10.1109/icsae.2016.7810174", + "doi": "https://doi.org/10.1109/icsae.2016.7810174", + "abstract": "Visual processing has attracted a lot of attention in the last decade. Hierarchical approaches for object recognition are gradually becoming widely-accepted. Generally, they are inspired by the ventral stream of human visual cortex, which is in charge of rapid categorization. Similar to objects, natural scenes share common features and can, therefore, be classified in the same manner. However, natural scenes generally show a high level of statistical correlation between classes. This, in fact, is a major challenge for most object recognition models. Rapid categorization of a natural scene in the absence of attention is a challenge. However, researchers have found that 150 ms is enough to categorize a complex natural scene. We tested the capability of our recent and bio-inspired En-HMAX model of visual processing for scene classification. The results show the En-HMAX model has a comparable performance to state of the art methods for natural scene categorization.", + "citation_count": 7, + "topics": [ + "Advanced Image and Video Retrieval Techniques", + "Remote-Sensing Image Classification", + "Image Retrieval and Classification Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4391598857" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2384649850", + "title": "A Review on Various E-Business and M-Business Models & Research Opportunities", + "year": 2016, + "url": "https://doi.org/10.5281/zenodo.161146", + "doi": "https://doi.org/10.5281/zenodo.161146", + "abstract": "", + "citation_count": 7, + "topics": [ + "Technology Adoption and User Behaviour" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1964976553", + "title": "Introductory computing course content", + "year": 2011, + "url": "https://doi.org/10.1145/2047594.2047610", + "doi": "https://doi.org/10.1145/2047594.2047610", + "abstract": "Selecting the appropriate content for introductory computing courses is an important part of attracting and retaining students in computer related education programs. This paper reports the results of an educator survey including SIGCSE and SIGITE members designed to evaluate topics that member institutions currently include and would prefer to include in their introductory computing courses notably for non-majors. In addition, we contrast information obtained from the educator surveys to a student preference survey and student outcome evaluations from our introductory computing course. In this paper we show how the perspectives of computer science and information technology educators differ with regard to content of their courses and the differences between the currently offered content and what would be preferred. We also examine survey respondent perspectives regarding the teaching of the office suite, broadening participation, and interdisciplinary topics.", + "citation_count": 7, + "topics": [ + "Open Education and E-Learning", + "Interdisciplinary Research and Collaboration", + "Information Systems Education and Curriculum Development" + ], + "discovered_via": [ + "neighbor:openalex:W4390315357" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4282983774", + "title": "Validity: Criterion, Concurrent, Ecological, and Predictive", + "year": 2022, + "url": "https://doi.org/10.4324/9780367198459-reprw156-1", + "doi": "https://doi.org/10.4324/9780367198459-reprw156-1", + "abstract": "Validity is a measure of how sound the instrument is. A valid instrument is one that measures what it states it measures and does not measure additional variables. Validity also applies to the interpretation. A valid study is one where the findings truly represent the variables you are claiming to measure. Therefore, the interpretations that are made based on the findings are appropriate given the design and method of the study. There are numerous types of validity: criterion, concurrent, ecological, and predictive. Criterion validity compares the test results with the outcome. This is commonly done by correlating the results of one assessment to the results of another assessment. Concurrent validity applies to the creation of a new test. To establish concurrent validity you would measure how well a new test compares to an established test. Ecological validity refers to how generalisable your study's results are to the real world. Predictive validity tells you how well a test can predict a characteristic of an individual. Reliability and validity are two terms commonly seen together. However, reliability and validity are distinct terms.", + "citation_count": 6, + "topics": [ + "Edcuational Technology Systems" + ], + "discovered_via": [ + "neighbor:openalex:W4409825334" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2946465479", + "title": "EXPLORING THE BEHAVIORAL INTENTION TO USE E-GOVERNMENT SERVICES: VALIDATING THE UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY", + "year": 2019, + "url": "https://doi.org/10.33965/its2019_201901l004", + "doi": "https://doi.org/10.33965/its2019_201901l004", + "abstract": "This study explores the online users\u0092 behavioral intention to utilize the governments\u0092 websites and their electronic services. The research methodology validates the measuring items from the unified theory of acceptance and use of technology", + "citation_count": 6, + "topics": [ + "Technology Adoption and User Behaviour", + "E-Government and Public Services", + "Social Media and Politics" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1557784891", + "title": "Corporate Governance: Frameworks", + "year": 2012, + "url": "https://doi.org/10.17485/ijst/2012/v5i9/30687", + "doi": "https://doi.org/10.17485/ijst/2012/v5i9/30687", + "abstract": "In this essay we have discussed the broad spectrum of definitions of corporate governance that exist in the literature, ranging from a narrow, agency theory definition to broader, stakeholder-oriented definitions. The definition for the purposes of this essay adopts a stakeholder-oriented approach to corporate governance and does not necessarily contradict an agency theory approach. The essay has also outlined three theoretical frameworks used for discussing and analyzing corporate governance and has examined the extent to which they overlap. Having outlined some theoretical issues in corporate governance, we now turn to a discussion of the practical agenda for corporate governance reform. Our first task is to look at what happens when corporate governance fails, as in the cases of Enron and Parmalat. These cases clearly demonstrate a need for corporate governance reform. The next task is to examine the ways in which corporate governance may be improved by targeting a range of mechanisms, checks and balances.", + "citation_count": 6, + "topics": [ + "Corporate Finance and Governance", + "Financial Reporting and Valuation Research", + "Law, Economics, and Judicial Systems" + ], + "discovered_via": [ + "neighbor:openalex:W4400303336" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1922252959", + "title": "Administrative Assumptions in Top\u2010Down Selection: A Test in Graduate School Admission Decisions", + "year": 2011, + "url": "https://doi.org/10.1111/j.1464-0597.2011.00480.x", + "doi": "https://doi.org/10.1111/j.1464-0597.2011.00480.x", + "abstract": "Although top\u2010down selection is the gold standard for making personnel decisions, several administrative assumptions must be met for it to be effective. We discuss three of these assumptions and test two of them: (1) top applicants will accept an offer, and (2) the time organisations give applicants to consider an offer will not influence the availability of next\u2010tier applicants. We also examine the effectiveness of top\u2010down selection by comparing it to an administratively simpler procedure, random selection above a threshold. Using archival admissions data from three university graduate psychology programs, we found that top applicants were less likely to accept an offer; however, waiting time did not influence applicant availability. In comparing the quality of applicants actually selected (with a top\u2010down procedure) with the quality of applicants selected at random (from above five progressively stringent thresholds), we found that at higher admission thresholds, random selection resulted in better or equal quality applicants as top\u2010down selection, depending on the criteria. We discuss implications for future research and practice.", + "citation_count": 6, + "topics": [ + "Employer Branding and e-HRM", + "Medical Education and Admissions", + "Diversity and Career in Medicine" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4252678288", + "title": "Modeling program predictability", + "year": 1998, + "url": "https://doi.org/10.1145/279361.279371", + "doi": "https://doi.org/10.1145/279361.279371", + "abstract": "Basic properties of program predictability --- for both values and control --- are defined and studied. We take the view that program predictability originates at certain points during a program's execution, flows through subsequent instructions, and then ends at other points in the program. These key components of predictability: generation, propagation, and termination; are defined in terms of a model. The model is based on a graph derived from dynamic data dependences and a predictor.Using the SPEC95 benchmarks, we analyze the predictability phenomena both separately and in combination. Examples are provided to illustrate relationships between model-based characteristics and program constructs. It is shown that most predictability derives from program control structure and immediate values, not program input data. Furthermore, most predictability originates from a relatively small number of generate points. The analysis of obtained results suggests a number of ramifications regarding predictability and its use.", + "citation_count": 6, + "topics": [ + "Parallel Computing and Optimization Techniques", + "Distributed systems and fault tolerance", + "Radiation Effects in Electronics" + ], + "discovered_via": [ + "neighbor:openalex:W4386142022" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4390549206", + "title": "Generative AI Takes Centre Stage: Revolutionizing Productivity and Reshaping Industries", + "year": 2023, + "url": "https://doi.org/10.2478/czoto-2023-0007", + "doi": "https://doi.org/10.2478/czoto-2023-0007", + "abstract": "Abstract The growing prominence of Generative AI in discussions on artificial intelligence has significant implications for productivity and industry dynamics. This article aims to examine the transformative role of Generative AI, specifically focusing on its revolutionary impact on productivity and its influence on various industries. The objectives of this article include conducting a detailed analysis of how systems have greatly enhanced efficiency for developers and knowledge workers. By examining both the positive and negative aspects of the Generative AI movement, this article aims to provide valuable insights into the innovations driven by Generative AI and the advancements that contribute to its evolution. Through this exploration, the goal is to offer a comprehensive understanding of the current landscape, highlighting the opportunities and challenges presented by the rise of Generative AI in the management sphere.", + "citation_count": 5, + "topics": [ + "Digital Transformation in Industry", + "Business and Economic Development" + ], + "discovered_via": [ + "neighbor:openalex:W4390315357", + "neighbor:openalex:W4391830676" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4378227617", + "title": "Students\u2019 Adoption of ICT Tools for Learning English Based on Unified Theory of Acceptance and Use of Technology", + "year": 2023, + "url": "https://doi.org/10.9734/ajess/2023/v44i3963", + "doi": "https://doi.org/10.9734/ajess/2023/v44i3963", + "abstract": "Information and communication technology (ICT) has advanced significantly over the past few decades, and students now frequently use ICT tools that enable them to learn anywhere and at any time. This study explored the influencing factors of the adoption of ICT tools among Chinese college students to learn English based on the unified theory of acceptance and use of technology (UTAUT). Structural equation modelling was applied to analyse sample data collected from 223 students in Zhaoqing University and Guangdong University of Finance in China. The results revealed that effort expectancy, performance expectancy and social influence exerted significant effect on behavioural intention. In addition, facilitating conditions and behavioural intention are positively and significantly related to use behaviour. The five variables, namely, effort expectancy, performance expectancy, social influence, facilitating conditions and behavioural intention explained 93.4% of the total variance in the ICT tools use behaviour to learn English by Chinese college students, confirming the validity of UTAUT in Chinese higher education context. Finally, some implications were provided to boost the adoption of ICT tools for promoting students\u2019 learning performance.", + "citation_count": 5, + "topics": [ + "Technology Adoption and User Behaviour" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4380081217", + "title": "Bibliometric Analysis of TPACK Publication Trends in Scopus Data Base from 2013 to 2022", + "year": 2023, + "url": "https://doi.org/10.46627/silet.v4i1.199", + "doi": "https://doi.org/10.46627/silet.v4i1.199", + "abstract": "This study aims to find out the development of the publication of scientific articles about the TPACK (Technological Pedagogic Content Knowledge) Model. The research method used was a literature review using a bibliometric analysis approach that started by searching the TPACK model keywords in the Scopus journal database from 2013 to 2022. The study results revealed that within a period of 10 years, from 2013 to 2022, there were 528 publications of the TPACK model published in Scopus-indexed journals. Meanwhile, the United States is the country that contributes the most to the number of published articles. The Journal of Physics Conference is listed as the journal that has contributed the most to the dissemination of the Scopus-indexed TPACK model articles. To get more comprehensive results, further research is not only limited to Scopus-indexed journals but also includes the integration of the TPACK component in the learning to be studied.", + "citation_count": 5, + "topics": [ + "Educational Assessment and Pedagogy", + "Technology-Enhanced Education Studies", + "Educational Curriculum and Learning Methods" + ], + "discovered_via": [ + "neighbor:openalex:W4316038168" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4308258967", + "title": "Defining the Wind Energy Workforce Gap", + "year": 2022, + "url": "https://doi.org/10.2172/1896898", + "doi": "https://doi.org/10.2172/1896898", + "abstract": "This report shares insights as to what the wind energy workforce gap is and why it exists, and how to inform industry, educational institutions, and the potential workforce so that they can reduce barriers and better connect the wind energy workforce through existing and novel pathways. The insights used in this report were collected from two web-based surveys:, one designed for industry and the other designed for the potential workforce.", + "citation_count": 5, + "topics": [ + "Social Acceptance of Renewable Energy" + ], + "discovered_via": [ + "neighbor:openalex:W4402876502", + "neighbor:openalex:W4407192387" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4226138107", + "title": "Digital transformation of enterprises: trends, factors, results", + "year": 2022, + "url": "https://doi.org/10.5377/nexo.v35i01.13924", + "doi": "https://doi.org/10.5377/nexo.v35i01.13924", + "abstract": "Goals. To identify methodological contradictions and inconsistencies between different approaches to measuring the digital economy as a whole, the digital transformation of enterprises; to assess the effectiveness of digital transformations, and to outline the contours of the modern paradigm of digital transformation management in the post-Covid world. Results. The changes in the conceptual approaches to the measurement of the digital economy and digital transformation due to new trends in the development of digital transformation have been revealed. It has been shown which factors and actions determine the results of the digital transformation of enterprises, based on which the modern paradigm of digital transformation management has been formed. The features of the national digital transformation strategy have been analyzed. Conclusion. In the new post-Covid reality, enterprises will change dramatically, and to achieve success they will need a new paradigm of digital transformation management, organically combining economic and social aspects, a strategic and operational approach, considering transformation management as a flexible self-learning ecosystem.", + "citation_count": 5, + "topics": [ + "Digital Economy and Transformation", + "Economic Development and Digital Transformation", + "Economic Issues in Ukraine" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4289529394", + "title": "DIGITAL TRANSFORMATION IN BUSINESSES: THE PROCESS AND ITS OUTCOMES", + "year": 2022, + "url": "https://doi.org/10.17261/pressacademia.2022.1599", + "doi": "https://doi.org/10.17261/pressacademia.2022.1599", + "abstract": "Purpose- The purpose of this study is to serve as an extensive outlook about digital transformation. Its content comprises the elements of digital transformation, the ways of adapting to digital transformation, reasons for failure, means of digital transformation, and insights and discussions on new business environment. Methodology- In this study, a comprehensive literature review is followed to learn about the current business circumstances regarding digital transformation and have a deep understanding on the previous studies conducted about digital transformation. Findings- The literature review reveals that digital transformation has provided positive impract on businesses at different levels. Although challenges against digital transformation may arise, they can be tackled if the nature of digital transformation is understood well. The success of digital transformation is dependent on numerous factors from different aspect which should be studied carefully before and during the adoption of digital transformation. Conclusion- It may be concluded that the COVID-19 pandemic has accelerated the digital agenda of businesses. At first, it should be understood that digital transformation is not a mere upgrade of technology or technical equipment within an organization but requires time, curiosity, creativity, recognition of opportunities, and cultural transformation. A successful adoption of digital transformation requires the recognition of means of digital transformation, the steps for adaptation to digital transformation, the analysis of failure, the outcomes of digital transformation. The recent evolutions related to digital transformation is evident in different aspects of business. The most recent observed changes in businesses are required skills of employees, organisational culture, business models, and customer relationship management practices. Keywords: Digital transformation, digitalization, new business environment, business success, data analysis. JEL Codes: M10, M14, M15", + "citation_count": 5, + "topics": [ + "Business and Economic Development", + "Sustainability and Innovation in Business", + "Digital Transformation in Industry" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3198113463", + "title": "To tune or not to tune? An Approach for Recommending Important Hyperparameters", + "year": 2021, + "url": "https://doi.org/10.48550/arxiv.2108.13066", + "doi": "https://doi.org/10.48550/arxiv.2108.13066", + "abstract": "Novel technologies in automated machine learning ease the complexity of algorithm selection and hyperparameter optimization. Hyperparameters are important for machine learning models as they significantly influence the performance of machine learning models. Many optimization techniques have achieved notable success in hyperparameter tuning and surpassed the performance of human experts. However, depending on such techniques as blackbox algorithms can leave machine learning practitioners without insight into the relative importance of different hyperparameters. In this paper, we consider building the relationship between the performance of the machine learning models and their hyperparameters to discover the trend and gain insights, with empirical results based on six classifiers and 200 datasets. Our results enable users to decide whether it is worth conducting a possibly time-consuming tuning strategy, to focus on the most important hyperparameters, and to choose adequate hyperparameter spaces for tuning. The results of our experiments show that gradient boosting and Adaboost outperform other classifiers across 200 problems. However, they need tuning to boost their performance. Overall, the results obtained from this study provide a quantitative basis to focus efforts toward guided automated hyperparameter optimization and contribute toward the development of better-automated machine learning frameworks.", + "citation_count": 5, + "topics": [ + "Machine Learning and Data Classification", + "Machine Learning and Algorithms", + "Data Stream Mining Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4387789492" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3021522055", + "title": "HR professionals: key drivers for implementing sustainable business practices", + "year": 2020, + "url": "https://doi.org/10.1108/shr-03-2020-0025", + "doi": "https://doi.org/10.1108/shr-03-2020-0025", + "abstract": "Purpose The purpose of this paper is to highlight the importance of sustainable management practices in the current era. The paper highlights the critical role that HR Professionals play in driving this sustainability agenda in organizations. Design/methodology/approach The paper delebrates on how the philosophy of various stakeholders has changed in recent years in embracing responsible management practices. The paper also discusses the role of HR Professionals in embedding and internationalizing a sustainable strategy into the organizational culture. It also discusses some ways with the help of suitable examples from the industry. Findings As globalization, ever-changing demographics and competition for the world\u2019s draining resources force transformational change, businesses would require leadership that is not only enlightened but sustainability-savvy as well to prosper. HR has a very critical role to play in aligning talent with these emerging realities. Originality/value This paper discusses how a growing number of organizations are acknowledging sustainability as a key differentiator for competitive advantage. Furthermore, it discusses the significance of HR Professionals in pushing the sustainability agenda in their organizations. It also presents the strategies and methods to achieve the same with the help of suitable examples.", + "citation_count": 5, + "topics": [ + "Leadership, Behavior, and Decision-Making Studies", + "Management and Organizational Studies", + "Complex Systems and Decision Making" + ], + "discovered_via": [ + "neighbor:openalex:W4392871535" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2743779113", + "title": "Processing occlusions using elastic-net hierarchical MAX model of the visual cortex", + "year": 2017, + "url": "https://doi.org/10.1109/inista.2017.8001150", + "doi": "https://doi.org/10.1109/inista.2017.8001150", + "abstract": "Humans can recognise objects under partial occlusion. Machine-based approaches cannot reliably recognise objects and scenes in the presence of occlusion. This paper investigates the use of the elastic net hierarchical MAX (En-HMAX) model to handle occlusions. Our experiments show that the En-HMAX model achieves an accuracy of ~70%, when ~50% artificial occlusions are applied to the centre of the visual object-field. Furthermore, when the same percentage of occlusion is applied to the peripheral, the model reports higher accuracies. A similar degree of robustness has been observed when recognising scenes. The results suggest that cortex-like models, such as the En-HMAX are reliable for solving the occlusion challenge.", + "citation_count": 5, + "topics": [ + "Tactile and Sensory Interactions", + "Motor Control and Adaptation", + "Optical Imaging and Spectroscopy Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4391598857" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2465767691", + "title": "An elastic net-regularized HMAX model of visual processing", + "year": 2015, + "url": "https://doi.org/10.1049/cp.2015.1753", + "doi": "https://doi.org/10.1049/cp.2015.1753", + "abstract": "The hierarchical MAX (HMAX) model of human visual system has been used in robotics and autonomous systems widely. However, there is still a stark gap between human and robotic vision in observing the environment and intelligently categorizing the objects. Therefore, improving models such as the HMAX is still topical. In this work, in order to enhance the performance of HMAX in an object recognition task, we augmented it using an elastic net-regularised dictionary learning approach. We used the notion of sparse coding in the S layers of the HMAX model to extract mid- and high-level, i.e. abstract, features from input images. In addition, we used spatial pyramid pooling (SPP) at the output of higher layers to create a fixed feature vectors before feeding them into a softmax classifier. In our model, the sparse coefficients calculated by the elastic net-regularised dictionary learning algorithm were used to train and test the model. With this setup, we achieved a classification accuracy of 82.6387%\u22133.7183% averaged across 5-folds which is significantly better than that achieved with the original HMAX.", + "citation_count": 5, + "topics": [ + "Neural dynamics and brain function", + "Neuroscience and Neuropharmacology Research", + "Neurotransmitter Receptor Influence on Behavior" + ], + "discovered_via": [ + "neighbor:openalex:W4391598857" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2084937878", + "title": "Corporate governance - a stakeholder model", + "year": 2012, + "url": "https://doi.org/10.1504/ijbge.2012.047536", + "doi": "https://doi.org/10.1504/ijbge.2012.047536", + "abstract": "Much analysis aimed at identifying the causes behind the financial crisis has led many to criticise corporate governance practices in the USA and the UK. Nonetheless relatively little attention has been paid to analysing the governance structure of companies as an underlying source of the crisis. To help fill this gap, this article offers a critique of the Anglo\u2013American model and draws on stakeholder theory to offer an alternative corporate governance model.", + "citation_count": 5, + "topics": [ + "Corporate Finance and Governance", + "State Capitalism and Financial Governance", + "Housing, Finance, and Neoliberalism" + ], + "discovered_via": [ + "neighbor:openalex:W4400303336" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1582146028", + "title": "A Study on Integrating Business Intelligence into E-Business", + "year": 2012, + "url": "https://doi.org/10.18517/ijaseit.2.6.239", + "doi": "https://doi.org/10.18517/ijaseit.2.6.239", + "abstract": "With the high speed of development in the information technology area, many organizations use the Internet in their businesses instead of traditional 'brick and mortar' physical stores. With the rapid spread of the Internet, people can access the web easily anywhere and anytime without barriers of time or space. However, a lot of data collected from global and local e-businesses is complex and unstructured and thus of limited use. Business intelligence tools could be used to extract accurate and useful information about future forecasts, business patterns, and business decision making. This paper provides a high level view of business intelligence and its benefits in e-business. Methods of adopting business intelligence are studied and examples of current market business intelligence tools are given in order to give a better idea of business intelligence usage in e-business.", + "citation_count": 5, + "topics": [ + "Big Data and Business Intelligence", + "Customer churn and segmentation", + "Technology Adoption and User Behaviour" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4400279324", + "title": "Construct Validity, Longitudinal Measurement Invariance, Incremental Validity, and Predictive Validity of the Original Grit Scale in Chinese Young Adults", + "year": 2024, + "url": "https://doi.org/10.1080/00223891.2024.2367547", + "doi": "https://doi.org/10.1080/00223891.2024.2367547", + "abstract": "Although many studies have attempted to validate grit scales because of the construct's popularity, most have considered the shorter rather than the longer Original Grit Scale (Grit-O). We examined the Grit-O's construct validity, longitudinal measurement invariance, incremental validity for academic performance, and longitudinal predictive validity for subjective well-being among young Chinese. We used a cross-sectional sample of 3,322 college students and a longitudinal sample of 1,884 college students, tested twice over 10 months. The first-order factor model fit the data better than other models and showed partial configural and metric measurement invariance over time. Grit and its two facets longitudinally predicted subjective well-being (i.e., life satisfaction, happiness, positive affect, negative affect, and depression) but had negligible incremental validity for two semesters' grades after controlling for conscientiousness. So, while the Grit-O could be a useful construct for young adults, its predictive value overlaps with a better-established construct, conscientiousness.", + "citation_count": 4, + "topics": [ + "Grit, Self-Efficacy, and Motivation", + "Resilience and Mental Health", + "Education and Learning Interventions" + ], + "discovered_via": [ + "neighbor:openalex:W4409825334" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3172254362", + "title": "Constructing AI: Examining how AI is shaped by data, models and people", + "year": 2021, + "url": "https://doi.org/10.29173/irie415", + "doi": "https://doi.org/10.29173/irie415", + "abstract": "Artificial Intelligence (AI) is a technology that is quickly becoming part of our digital infrastructure and woven into aspects of daily life. AI has the potential to impact society in many positive ways. However, there are numerous examples of AI systems that are operating in ways that are harmful, unjust and discriminatory. AI systems are constructs of the choices made in their design. They exist within a socio-cultural context that reflects the data used in their training, the design of their mathematical models and the values of their creators. If we want to build AI systems that benefit society, we need to change how we construct AI.", + "citation_count": 4, + "topics": [ + "Ethics and Social Impacts of AI" + ], + "discovered_via": [ + "neighbor:openalex:W2992233710" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2803396544", + "title": "Workforce Trends and Challenges", + "year": 2018, + "url": "https://doi.org/10.4018/978-1-5225-4933-8.ch003", + "doi": "https://doi.org/10.4018/978-1-5225-4933-8.ch003", + "abstract": "In this chapter, the author has reviewed the human workforce of the previous generations by taking into account the features and characteristics of the workforce, which is getting older. The purpose of this chapter is to evaluate the future of the current workforce. The future generation is still unexplored, but it is clear that the coming generation will be a blend of advanced technology and ultra-advanced simulations. The coming years will introduce more advanced artificial intelligence into the workforce that will not just be cognitively intelligent in a retrospective way but also emotionally intelligent. The future human workforce will face a challenge to maintain the requisite skill sets to cope with the constant change.", + "citation_count": 4, + "topics": [ + "Labor market dynamics and wage inequality", + "Retirement, Disability, and Employment", + "Employment and Welfare Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4402876502", + "neighbor:openalex:W4407192387" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4232682384", + "title": "Corporate governance: Frameworks", + "year": 2012, + "url": "https://doi.org/10.17485/ijst/2012/v5i9.23", + "doi": "https://doi.org/10.17485/ijst/2012/v5i9.23", + "abstract": "In this essay we have discussed the broad spectrum of definitions of corporate governance that exist in the literature, ranging from a narrow, agency theory definition to broader, stakeholder-oriented definitions. The definition for the purposes of this essay adopts a stakeholder-oriented approach to corporate governance and does not necessarily contradict an agency theory approach. The essay has also outlined three theoretical frameworks used for discussing and analyzing corporate governance and has examined the extent to which they overlap. Having outlined some theoretical issues in corporate governance, we now turn to a discussion of the practical agenda for corporate governance reform. Our first task is to look at what happens when corporate governance fails, as in the cases of Enron and Parmalat. These cases clearly demonstrate a need for corporate governance reform. The next task is to examine the ways in which corporate governance may be improved by targeting a range of mechanisms, checks and balances.Keywords: Corporate governance , Agency theory , cost theory", + "citation_count": 4, + "topics": [ + "Corporate Governance and Law" + ], + "discovered_via": [ + "neighbor:openalex:W4400303336" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1973428094", + "title": "Azo Dye-doped Polymer Films for Optical Limiting and Optical Phase Conjugation", + "year": 2012, + "url": "https://doi.org/10.1364/photonics.2012.wpo.3", + "doi": "https://doi.org/10.1364/photonics.2012.wpo.3", + "abstract": "Nonlinear optical properties, optical limiting and Optical phase conjugation through degenerate four-wave mixing is observed in Disperse Orange-25 dye-doped in Polymethyl methacrylate \u2013 metacrylic acid (PMMA-MA) polymer films under low-power, continuous-wave laser irradiation. A maximum phase conjugate efficiency of 0.22% has been obtained for probe beam intensity at 0.11 W/cm2. Phase conjugation is observed for both parallel- and orthogonally-polarized probe and pump beams. The maximum PC reflectivity is achieved when the angle between probe beam and forward pump beam is 7 degrees. The effects of dye concentration, inter beam angle between probe and forward pump beam on phase conjugation reflectivity are also studied. PC signal strength first increases and then decreases with time. PC reflectivity is also increased by increasing the intensity of the backward and forward pump beam. The predominant phase conjugation signal is attributed to the facts that reverse saturable absorption and large third order susceptibility of the dye molecules.", + "citation_count": 4, + "topics": [ + "Nonlinear Optical Materials Studies", + "Photorefractive and Nonlinear Optics", + "Nonlinear Optical Materials Research" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2041106304", + "title": "Performance differences on an untimed test: some Australian and New Zealand data from high school pupils and job applicants", + "year": 1994, + "url": "https://doi.org/10.1016/0191-8869(94)90222-4", + "doi": "https://doi.org/10.1016/0191-8869(94)90222-4", + "abstract": "", + "citation_count": 4, + "topics": [ + "Cognitive Abilities and Testing" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4296898177", + "title": "The Importance Of Human Resources (Hr) Management In Company", + "year": 2022, + "url": "https://doi.org/10.36418/jws.v1i9.89", + "doi": "https://doi.org/10.36418/jws.v1i9.89", + "abstract": "Introduction: Human resources have an essential role in realizing development. Human quality is a top priority in a company. So the need for human resource management in managing human resources in the company. Thus, this study aims to understand the importance of human resource management in a company. Methods: The method used is library research based on a qualitative approach. This literature study is to obtain data researchers collect, analyze, and organize, sources derived from articles, books, reports, and other research on human resource management. Results: The results obtained in this study are the importance of human resource management because it benefits the company, including 1) companies can utilize existing human resources competently, 2) productivity of existing employees is increasing, and 3) determination of employee needs/ labor. 4) handling of employment information, and 5) research. Conclusion: Human resource management is essential because it benefits the company, including the company can utilize existing human resources well, the productivity of existing employees is increasing, determining the needs of employees/labor\u2014handling of employment information, and research.", + "citation_count": 3, + "topics": [ + "Employee Performance and Management" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734", + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4312465432", + "title": "The Importance Of Human Resources (Hr) Management In Company", + "year": 2022, + "url": "https://doi.org/10.58344/jws.v1i9.89", + "doi": "https://doi.org/10.58344/jws.v1i9.89", + "abstract": "Introduction: Human resources have an essential role in realizing development. Human quality is a top priority in a company. So the need for human resource management in managing human resources in the company. Thus, this study aims to understand the importance of human resource management in a company. Methods: The method used is library research based on a qualitative approach. This literature study is to obtain data researchers collect, analyze, and organize, sources derived from articles, books, reports, and other research on human resource management. Results: The results obtained in this study are the importance of human resource management because it benefits the company, including 1) companies can utilize existing human resources competently, 2) productivity of existing employees is increasing, and 3) determination of employee needs/ labor. 4) handling of employment information, and 5) research. Conclusion: Human resource management is essential because it benefits the company, including the company can utilize existing human resources well, the productivity of existing employees is increasing, determining the needs of employees/labor\u2014handling of employment information, and research.", + "citation_count": 3, + "topics": [ + "Employee Performance and Management" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734", + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4280563792", + "title": "No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL", + "year": 2022, + "url": "https://doi.org/10.48550/arxiv.2205.08716", + "doi": "https://doi.org/10.48550/arxiv.2205.08716", + "abstract": "The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters. In real-world settings like robotics or industrial control systems, however, testing different hyperparameter configurations directly on the environment can be financially prohibitive, dangerous, or time consuming. We propose a new approach to tune hyperparameters from offline logs of data, to fully specify the hyperparameters for an RL agent that learns online in the real world. The approach is conceptually simple: we first learn a model of the environment from the offline data, which we call a calibration model, and then simulate learning in the calibration model to identify promising hyperparameters. We identify several criteria to make this strategy effective, and develop an approach that satisfies these criteria. We empirically investigate the method in a variety of settings to identify when it is effective and when it fails.", + "citation_count": 3, + "topics": [ + "Data Stream Mining Techniques", + "Machine Learning and Data Classification", + "Mobile Crowdsensing and Crowdsourcing" + ], + "discovered_via": [ + "neighbor:openalex:W4387789492" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3199956003", + "title": "Inkludering och s\u00e4rskild beg\u00e5vning. F\u00f6ruts\u00e4ttningar och dilemman i r\u00e5dgivande policydokument", + "year": 2021, + "url": "https://doi.org/10.48059/uod.v30i1.1553", + "doi": "https://doi.org/10.48059/uod.v30i1.1553", + "abstract": "Inclusion and Giftedness. Prerequisites and Dilemmas in Advisory Education Policy. This article aims to increase knowledge about central tensions regarding the educational policy of inclusion. We do this using giftedness as an example. Our analysis of advisory policy documents shows that gifted students are represented as being at risk, suffering, and alienated from other students. This has three consequences: first, it reproduces a polarisation between \u201cnormal\u201d and \u201cdifferent\u201d students, second, giftedness becomes a problem in schooling; and third, paradoxically the exclusion of the students is made a prerequisite for their inclusion. We conclude that the formulation of giftedness in the documents analysed illustrates central tensions within inclusion as policy, both regarding who is in focus, how inclusion is organised, and its inherent relationship to special education. We argue that more coherent policy-visions of inclusion are needed to address the needs of all students.", + "citation_count": 3, + "topics": [ + "Social and Educational Sciences", + "Early Childhood Education and Development", + "Youth Education and Societal Dynamics" + ], + "discovered_via": [ + "neighbor:openalex:W4366989571" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3213768676", + "title": "Digital Transformation of Slovenian Enterprises", + "year": 2021, + "url": "https://doi.org/10.23919/mipro52101.2021.9596708", + "doi": "https://doi.org/10.23919/mipro52101.2021.9596708", + "abstract": "The emergence of disruptive digital technologies in the last decade has ushered in the era of digital transformation. Although digital technologies offer numerous opportunities, many enterprises still lag behind when it comes to building the necessary digital capabilities for successful digital transformation. To enable wider adoption of digital technologies and accelerate the digital transformation of enterprises, several measures are required. In order to create the necessary supporting ecosystems at both European and national level, it is important to gain a deeper understanding of the challenges faced by enterprises. For this purpose, we created the questionnaire in cooperation with the University of Maribor, the Chamber of Commerce and Digital Innovation Hub Slovenia. The questionnaire was completed by 125 enterprises in Slovenia. In this paper, we present research results that illustrate the status of digital transformation among enterprises in Slovenia and provide guidance for developing appropriate measures to accelerate the digital transformation of enterprises.", + "citation_count": 3, + "topics": [ + "Digital Transformation in Industry", + "Big Data and Business Intelligence", + "Collaboration in agile enterprises" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2012795729", + "title": "Mastering Chinese business model by conducting indigenous management research", + "year": 2013, + "url": "https://doi.org/10.1108/cms-05-2013-0085", + "doi": "https://doi.org/10.1108/cms-05-2013-0085", + "abstract": "Purpose The purpose of this paper is to explore the topic of the Chinese business model and provide a brief introduction to the papers in this special issue. Design/methodology/approach The paper explores the importance of studying business model in the Chinese market and summarises the eight papers in this issue. Findings The papers in this special issue address what the business model is in China, how it influences organizational outcomes, and how to build the business model in the Chinese market. These papers may be germane to both researchers and practitioners concerned with managing business model in China. For scholars, these papers broaden the understanding of business model and extend the theoretical boundaries of the business model from the Western to the Eastern context. For practitioners doing business in China, these papers give implications on how to build business models in China as well as what institutional and technological factors should be considered when designing their business models. Research limitations/implications Further investigation is needed into the interaction among Chinese firms' activities on business model design and operation and the institutional, technological, and market environment. Originality/value Overall, these papers make remarkable contributions to our understanding of Chinese business model. The topics of these studies are various and the methods are also multiplex. Several interesting Chinese business models are addressed such as Bandit business model and \u201ca company + farmers\u201d.", + "citation_count": 3, + "topics": [ + "Family Business Performance and Succession", + "Global and Cross-Cultural Management", + "Corporate Finance and Governance" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4253467046", + "title": "Two Experiments in Reducing Overconfidence in Spreadsheet Development", + "year": 2009, + "url": "https://doi.org/10.4018/978-1-60566-136-0.ch009", + "doi": "https://doi.org/10.4018/978-1-60566-136-0.ch009", + "abstract": "This chapter describes two experiments that examined overconfidence in spreadsheet development. Overconfidence has been seen widely in spreadsheet development and could account for the rarity of testing by end-user spreadsheet developers. The first experiment studied a new way of measuring overconfidence. It demonstrated that overconfidence really is strong among spreadsheet developers. The second experiment attempted to reduce overconfidence by telling subjects in the treatment group the percentage of students who made errors on the task in the past. This warning did reduce overconfidence, and it reduced errors somewhat, although not enough to make spreadsheet development safe.", + "citation_count": 3, + "topics": [ + "Spreadsheets and End-User Computing", + "Statistics Education and Methodologies" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2402201421", + "title": "An Immodest Proposal: Pay Equity for Nursing Faculty Who Do Clinical Teaching", + "year": 1992, + "url": "https://doi.org/10.3928/0148-4834-19920501-07", + "doi": "https://doi.org/10.3928/0148-4834-19920501-07", + "abstract": "Pay equity, the concept of equal pay for equal or comparable work, will continue to be of paramount importance to women as the 20th century draws to a close. While it might have been anticipated that women in academic settings would enjoy pay equity, clinical teaching in nursing education provides a model for gender discrimination as related to women's work. Elements of proposal development and a case study for contesting pay inequity are presented.", + "citation_count": 3, + "topics": [ + "Gender Diversity and Inequality" + ], + "discovered_via": [ + "neighbor:openalex:W4385952870" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4390421286", + "title": "Method for Hyperparameter Tuning of EfficientNetV2-based Image Classification by Deliberately Modifying Optuna Tuned Result", + "year": 2023, + "url": "https://doi.org/10.14569/ijacsa.2023.0141248", + "doi": "https://doi.org/10.14569/ijacsa.2023.0141248", + "abstract": "Method for hyperparameter tuning of EfficientNetV2-based image classification by deliberately modifying Optuna tuned result is proposed. An example of the proposed method for textile pattern quality evaluation (good or bad textile pattern fluctuation quality classification) is shown. When using the hyperparameters obtained by Optuna without changing them, the accuracy certainly improved. Furthermore, as a result of learning by changing the hyperparameter with the highest degree of importance, the accuracy changed, so it could be said that the degree of importance was certainly high. However, the accuracy also changes when learning is performed by changing the least important hyperparameter, and sometimes the accuracy is improved compared to when learning is performed using the optimal hyperparameter. From this result, it is found that the optimal hyperparameters obtained with Optuna are not necessarily optimal.", + "citation_count": 2, + "topics": [ + "Industrial Vision Systems and Defect Detection", + "Neural Networks and Applications" + ], + "discovered_via": [ + "neighbor:openalex:W4387789492" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4322745238", + "title": "The Epistemology of Transformative Learning: A Systematic Instructional Design Framework Model in Transformative Learning", + "year": 2023, + "url": "https://doi.org/10.26737/ij-mds.v6i1.3545", + "doi": "https://doi.org/10.26737/ij-mds.v6i1.3545", + "abstract": "<em>Research in transformative learning has flourished in educational research in the latest decades. Among the research on transformative learning, the specific topic of how to develop instruction based on transformative learning is still very rarely found. The purpose of this study w</em><em>as</em><em> to review the epistemology of transformative learning by introducing a systematic ID framework for transformative learning. </em><em>The method of this study w</em><em>as</em><em> the systematic literature review with three review stages: planning the review, conducting the review, and reporting the review. The review was focused on the academic paper in the research scope of transformative learning. As the result, we concluded the 3 main components of designing a transformative learning model: (1) transformative outcomes, (2) transformative scaffolding, and (3) transformative learning experience. The study also proposed 7 stages of systematic ID framework in designing transformative learning.</em>", + "citation_count": 2, + "topics": [ + "Adult and Continuing Education Topics", + "Education and Critical Thinking Development", + "Gender, Education, and Development Issues" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4322096794", + "title": "Self-assessment of Digital Transformation", + "year": 2023, + "url": "https://doi.org/10.1007/978-981-19-9111-0_7", + "doi": "https://doi.org/10.1007/978-981-19-9111-0_7", + "abstract": "", + "citation_count": 2, + "topics": [ + "Digital Transformation in Industry", + "Flexible and Reconfigurable Manufacturing Systems" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4280521274", + "title": "Bias Amplification in Gender, Gender Identity, and Geographical Affiliation", + "year": 2022, + "url": "https://doi.org/10.1021/acs.jcim.2c00533", + "doi": "https://doi.org/10.1021/acs.jcim.2c00533", + "abstract": "In the quest for greater equity in science, individual attitudes and institutional policies should also embrace greater diversity and inclusion of minority groups. This viewpoint calls for a broader definition of gender bias in STEM to include gender identity and for increased attention to the issue of bias amplification due to geographic affiliation in the field of computational chemistry and chemoinformatics. It briefly discusses some active interventions to tackle bias on gender, gender identity, and geographic affiliation in STEM.", + "citation_count": 2, + "topics": [ + "Career Development and Diversity", + "Diversity and Career in Medicine", + "Advanced Causal Inference Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4385952870" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3137171911", + "title": "Complexity and Relative Complexity in Generative Grammar", + "year": 2021, + "url": "https://doi.org/10.3389/fcomm.2021.614352", + "doi": "https://doi.org/10.3389/fcomm.2021.614352", + "abstract": "The notions of \u201ccomplexity\u201d and its antonym \u201csimplicity\u201d have played an important role in the history of generative grammar. However, these terms have been used in different ways. There have been discussions about whether the raw data is complex (or not), about whether a particular theory is complex (or not), and about whether a particular analysis is complex (or not). This article both sorts out the various uses of these terms in the history of generative grammar and demonstrates that motivations have changed over time for whether a complex theory or a simple theory is more desirable. The article concludes with a discussion of the issue of relative complexity in generative grammar, that is, whether the theory embodies the possibility that a grammar of one language can be more or less complex than the grammar of another.", + "citation_count": 2, + "topics": [ + "Historical Linguistics and Language Studies", + "Syntax, Semantics, Linguistic Variation", + "Linguistic Variation and Morphology" + ], + "discovered_via": [ + "neighbor:openalex:W4390315357", + "neighbor:openalex:W4391830676" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3136018819", + "title": "An Examination of Factors Influencing Government Employees to Adopt and Use Social Media", + "year": 2021, + "url": "https://doi.org/10.1007/978-3-030-71292-1_32", + "doi": "https://doi.org/10.1007/978-3-030-71292-1_32", + "abstract": "", + "citation_count": 2, + "topics": [ + "E-Government and Public Services", + "Technology Adoption and User Behaviour", + "Knowledge Management and Sharing" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2891538753", + "title": "Understanding business model innovation : Recommendations for future business model tooling by an action research", + "year": 2018, + "url": "http://resolver.tudelft.nl/uuid:722928ec-3afb-4b9b-a7dc-4d72a39e9923", + "doi": null, + "abstract": "In Research and Development (R&amp;D) projects, Business Model Innovation (BMI) is a challenging issue given the high degrees of uncertainty regarding technologies, products and markets. The purpose of this research is to investigate how business model exploration is facilitated with the use of business model tooling and what are the gaps in the current business model repository in supporting business model exploration as part of the BMI process. These gaps can be addressed in the design of future business model tooling. We do so by supporting the business model exploration within a specific complex setting, introducing changes in the business models, and evaluating their effects. For 12 months we actively participated in an R&amp;D project funded by European Union focused on building a digital service that will improve mobility safety. We observed that business model exploration is an indispensable process when the value offering is not clearly defined, or when there are new business opportunities. Based on the observations, our recommendations regarding business model exploration are: (a) to start with an initial business model even if the final offering is not clear, (b) to create alternative business models and explore their potentials, and (c) use tools or practices that can contribute on the decision making regarding BMI.", + "citation_count": 2, + "topics": [ + "Business Process Modeling and Analysis", + "Digital Innovation in Industries", + "Information Technology Governance and Strategy" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2767354473", + "title": "Gender diversity and organizational performance: a study of IT industries in Bangalore", + "year": 2017, + "url": "https://doi.org/10.21511/im.13(3).2017.04", + "doi": "https://doi.org/10.21511/im.13(3).2017.04", + "abstract": "Humans are considered to be different from each other with their acumen and intelligence. The working condition in IT sector has changed over the past few decades. There has been a drastic increase in the number of female employees towards the development of IT sector over the past few years. Gender diversity is creating a wide range of awareness and helps understand the importance of gender identity. IT industry which is a dominant industry in India has reckoned gender diversity as a major tool to ensure it stands on criteria of being competent and innovative in the ever changing dynamic business environment. The main objective of this paper is to analyze the relationship between acceptance of gender diversity among the employees, diversity practices and programs adopted by the IT industries and barriers to the same.", + "citation_count": 2, + "topics": [ + "Gender Diversity and Inequality", + "Labor market dynamics and wage inequality", + "Work-Family Balance Challenges" + ], + "discovered_via": [ + "neighbor:openalex:W4322742241" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2604405318", + "title": "The use of marketing in human resource management", + "year": 2016, + "url": "https://doi.org/10.18844/gjbem.v6i2.1375", + "doi": "https://doi.org/10.18844/gjbem.v6i2.1375", + "abstract": "One of the decisive factors of the long-term functioning and effectiveness of any company business is human resources. The article discusses the approaches and insights on human resources management through the eyes of various authors and explains that human resources are not linked only to the field of management but also in areas such as planning, searching, selection, evaluation, motivation, training, remuneration and of course the release of human resources. The globalized market sharpens competition and human resources management can decide if the company stands on the side of winners. The aim of the article is to assess how companies in the Slovak economy cope with the challenges raised by human resources. Based on the realized survey aimed at identifying the current level of human resources management in organizations operating in Slovakia for the years 2010 - 2012 will analyze and evaluate the current status level of human resources. The gaps in research problems will be identified and will be provided recommendations for improvement of activities related to human resources and enhancing the competitive position of the company through personnel marketing tools. A personnel marketing is used primarily for search and retrieval quality people, their stabilization and strengthening their solidarity with the company. An application of personnel marketing leads to specific staff and corporate culture. Keywords: Human resource, Recruitment of employees, Personnel marketing; ", + "citation_count": 2, + "topics": [ + "Business and Economic Development", + "Employer Branding and e-HRM", + "Labor Market and Education" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734", + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W326615706", + "title": "Improving Personnel Selection through Frame of Reference Effect on Personality Inventory: Predictive and Incremental Validity over Cognitive Ability and Job Knowledge", + "year": 2015, + "url": "https://doi.org/10.1016/j.sbspro.2015.03.049", + "doi": "https://doi.org/10.1016/j.sbspro.2015.03.049", + "abstract": "Improving predictive validity of personality inventory in the context of personnel selection is an actual concern among researchers. We tested the effect of frame of reference (FOR) on the predictive validity of a Big Five measure in the context of personnel selection, where other two predictors were contained. The sample size was realistic for a small business enterprise (N = 36). Agreeability was the only dimension showing predictive validity (FOR effect). Results suggested also that narrow dimensions were more useful predictors in the contextualized measure of personality adding incremental validity over cognitive ability and job knowledge.", + "citation_count": 2, + "topics": [ + "Personality Traits and Psychology", + "Emotional Intelligence and Performance", + "Job Satisfaction and Organizational Behavior" + ], + "discovered_via": [ + "neighbor:openalex:W4409825334" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2072812638", + "title": "Revealing Originality of Song Works: An Analysis to the Copyright Law", + "year": 2014, + "url": "https://doi.org/10.15742/ilrev.v4n3.121", + "doi": "https://doi.org/10.15742/ilrev.v4n3.121", + "abstract": "The topic of this paper is to describe the defining criteria of originality of song works. The aspect of originality is important to make such work be protected by Copyright Law. In this research, the criteria to define originality are based on certain doctrines and/or theories of originality that may vary case by case. The use of such doctrines and/or theories are necessary, since the stipulations regarding originality in the Indonesian Copyright Act has not been considered suffice. With regard to the song works, the criteria of originality may be different from other works. Therefore, a comprehensive research on the characteristics of song as a work is also important. This research is a qualitative research with prescriptive design. The research depicts the use of certain doctrines and/or theories as supplementary provisions to the Copyright Law in defining the originality of songs, which have specific characteristics resulted from their author\u2019s creations and intellectuals.", + "citation_count": 2, + "topics": [ + "Legal Studies and Policies", + "Indonesian Legal and Regulatory Studies", + "Legal and Social Justice Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4381684347" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1985151894", + "title": "Transparency obligation for holders of EU IP assets in the pharmaceutical industry", + "year": 2014, + "url": "https://doi.org/10.1093/jiplp/jpt236", + "doi": "https://doi.org/10.1093/jiplp/jpt236", + "abstract": "A recent decision by the European Court of Justice (ECJ) in AstraZeneca v Commission (C-457/10 P), which has significant implications for intellectual property holders including IP license-holders, has not attracted much comment in the \u201cIP expert\u201d field. The implications are primarily on increased transparency obligations in European patent and IP procurement procedures (and by extension, on IP enforcement procedures) that may be, in certain situations, more disturbing than the existing transparency obligations in the US IP law. In particular, pharmaceutical companies are affected because of the narrow interpretation of the product market in the pharmaceutical industry by EU competition jurisprudence.", + "citation_count": 2, + "topics": [ + "International Arbitration and Investment Law", + "Intellectual Property and Patents" + ], + "discovered_via": [ + "neighbor:openalex:W4318407310" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2399184610", + "title": "Extracting Requirements for Ubiquitous Computing Technology-based IS Using Factor Analysis.", + "year": 2004, + "url": "https://aisel.aisnet.org/iceb2004/151", + "doi": null, + "abstract": "The paper describes a methodology for extracting the requirements for ubiquitous computing technology-based information system (IS) using factor analysis. The early idea of ubiquitous computing was articulated by Mark Weiser in 1988 and since then has been constantly developed. However, so far there was no any or few research on application of ubiquitous computing to IS. Prior to the development of a ubiquitous computing-based IS model necessary requirements are to be identified. To derive the requirements we refer to conventional research on ubiquitous computing, ubiquitous service and ubiquitous environment, and hence, to their characteristics. Having specified those requirements using factor analysis technique it will be easier to develop a consistent ubiquitous computing-based IS.", + "citation_count": 2, + "topics": [ + "Context-Aware Activity Recognition Systems", + "IoT and Edge/Fog Computing", + "Recommender Systems and Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4390315357" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2397653702", + "title": "[The guideline for the treatment of mood disorders in USA and Japan].", + "year": 2001, + "url": "https://pubmed.ncbi.nlm.nih.gov/11519148", + "doi": null, + "abstract": "Recently, the number of available antidepressants has increased dramatically and psychopharmacological treatment is becoming complex. It is important to present some guideline for supporting clinical decision making. Three different kinds of guideline for the treatment of mood disorders, that is, the APA style guideline, the algorithm and the consensus guideline, have been developed in our country. The APA style guideline and the algorithm are basically evidence based and the consensus guideline is developed through the consensus panel format. These guidelines should be used as 'a starting point' for specifying decisions that will be modified occasionally.", + "citation_count": 2, + "topics": [ + "Clinical practice guidelines implementation" + ], + "discovered_via": [ + "neighbor:openalex:W4407173730" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4321464266", + "title": "Gender quotas on boards 20 years on: a useful tool for increased wider diversity? Achievements, \"broken promises\" and blindspots in the Norwegian board diversity debate", + "year": 2023, + "url": "https://doi.org/10.4337/9781800377783.00012", + "doi": "https://doi.org/10.4337/9781800377783.00012", + "abstract": "Gender is a dimension of diversity, but diversity is more than gender. In this chapter we comment on the board diversity debate in Norway following the acceptance/introduction of the gender quota law in 2003/2006. The meaning of board diversity can be considered contextual, contested and temporal (see Lombardo et al 2010, Tatli et al. 2012). In the Norwegian context, we argue that while there was an implicit expectation that the gender quota law for Public Limited Companies (PLC) would increase diversity both within and beyond the PLC boards, the board diversity debate and achievements in Norway have been and still are predominantly focusing on gender and the PLC context. We argue that there is a need for a wider board diversity debate and focus from organisations, politicians and in research", + "citation_count": 1, + "topics": [ + "Gender Diversity and Inequality", + "Corporate Finance and Governance" + ], + "discovered_via": [ + "neighbor:openalex:W4322742241" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4384486036", + "title": "Crowdsourcing as a Future Collaborative Computing Paradigm", + "year": 2023, + "url": "https://doi.org/10.1007/978-3-031-32397-3_1", + "doi": "https://doi.org/10.1007/978-3-031-32397-3_1", + "abstract": "", + "citation_count": 1, + "topics": [ + "Mobile Crowdsensing and Crowdsourcing", + "Open Source Software Innovations", + "Data Stream Mining Techniques" + ], + "discovered_via": [ + "neighbor:openalex:W4401533174" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4389670110", + "title": "Enhancing the Human-Machine Interface: An Analysis of Unmanned Aerial Systems (UAS) Using the System Usability Scale (SUS)", + "year": 2023, + "url": "https://doi.org/10.5753/ihc_estendido.2023.233145", + "doi": "https://doi.org/10.5753/ihc_estendido.2023.233145", + "abstract": "This study assessed the usability of an Interactive System for Unmanned Aerial Systems (UAS) using the System Usability Scale (SUS). A simulation-based experiment involved 24 pilots with varying experience levels. Results showed an average SUS score of 67.4, indicating reasonably positive usability perception. Participants without prior piloting or aeronautical experiences scored lower, while those with military or technical backgrounds scored higher. These findings highlight the importance of considering user backgrounds for effective UAS control and offer insights for usability improvements.", + "citation_count": 1, + "topics": [ + "Human-Automation Interaction and Safety", + "Aerospace and Aviation Technology", + "Air Traffic Management and Optimization" + ], + "discovered_via": [ + "neighbor:openalex:W4392182430" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4297808784", + "title": "Examining Onboarding Experience through the Lens of New Employees\u2019 Expectations after Covid-19: An Empirical Study", + "year": 2022, + "url": "https://doi.org/10.18137/cardiometry.2022.23.780787", + "doi": "https://doi.org/10.18137/cardiometry.2022.23.780787", + "abstract": "Onboarding is an established process in an organization that can span from 3 days to 1 year. Onboarding entails functional induction and professional socialization of the new employee in the company\u2019s environment. It helps the employee to learn about the behaviors and expectations required for the position. Even after that, the outcomes of Onboarding are mostly not realized. The past and recent efforts on Onboarding are mostly related to creating a successful Onboarding process and has been unidirectional. This paper addresses the expectation of new employees joining the manufacturing industry in a management role after covid-19, and how integrating new employee\u2019s expectations with the Onboarding process will help enhance employee\u2019s experience during Onboarding. The paper also considers the key factors that will enable a new employee to increase efficiency and reduce the turnaround time. In all, the study stitches the various best practices in the industry and employee\u2019s expectations from Onboarding, bringing out a fruitful outcome.", + "citation_count": 1, + "topics": [ + "Organizational and Employee Performance", + "COVID-19 Pandemic Impacts", + "Cyberloafing and Workplace Behavior" + ], + "discovered_via": [ + "neighbor:openalex:W4392871535" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4294536920", + "title": "Socioeconomic status and mental health", + "year": 2022, + "url": "https://doi.org/10.1016/b978-0-323-91497-0.00131-4", + "doi": "https://doi.org/10.1016/b978-0-323-91497-0.00131-4", + "abstract": "", + "citation_count": 1, + "topics": [ + "Early Childhood Education and Development", + "Child and Adolescent Psychosocial and Emotional Development", + "Health disparities and outcomes" + ], + "discovered_via": [ + "neighbor:openalex:W4399518864" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3174876210", + "title": "Teaching transparency: principles and practical considerations with illustrations in R and Stata", + "year": 2021, + "url": "https://doi.org/10.4337/9781839101212.00017", + "doi": "https://doi.org/10.4337/9781839101212.00017", + "abstract": "This chapter advocates incorporating principles and practices of research transparency and reproducibility into undergraduate research methods courses. These principles and practices are increasingly essential and should be incorporated into methods courses as early as possible in order to develop an awareness of transparency and good habits. The chapter proceeds in five sections: (1) a clarification about what constitutes transparency; (2) a discussion of why transparency is important, including a the ongoing reproducibility crisis across disciplines, as well as material and nonmaterial motivations for transparency; (3) practices of transparency, including illustrations in R and Stata; and (5) a discussion of implementation tradeoffs. The conclusion closes with a discussion of the ongoing debates about transparency in our discipline and the need for a diversity of approaches.", + "citation_count": 1, + "topics": [ + "Data Analysis with R" + ], + "discovered_via": [ + "neighbor:openalex:W4386714740" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4200016632", + "title": "Design on Business Strategy under Value Creation System of Energy Internet", + "year": 2021, + "url": "https://doi.org/10.1109/icpre52634.2021.9635364", + "doi": "https://doi.org/10.1109/icpre52634.2021.9635364", + "abstract": "The innovative development of Energy Internet is constantly changing and reshaping the development pattern of manufacturing and other related industries, bringing many emerging values to economic and social development. The value creation system in the field of energy Internet includes three parts: the value proposition derived from traditional value to emerging value, the value activities with emerging business as the main body, and the value realization with business model as the main focus, which makes it different from the value creation methods of traditional energy and electricity. And the business model is one of the key links of the energy Internet, is an important way and way to realize value. Based on the value theory and combined with the logic of energy Internet value creation and its business model, this paper deconstructs and analyzes the value creation system of energy Internet and puts forward the business model strategy under the value creation system. Three emerging values of energy transformation services, energy digital products, and energy platform ecology are identified and their corresponding three emerging business activities are identified. Thus, three business models under the value creation system of energy Internet are proposed: service-led, technology-driven, and platform ecology. This paper further adopts the existing business model design methodology, constructs a simplified business model strategy analysis and design method, proposes three business strategies of product, capital and operation. This paper innovatively constructs the energy Internet value creation system architecture, puts forward the business model strategy analysis framework, identifies the emerging value, emerging business and new business model, and provides direction and guidance for the energy Internet value creation and the emergence of business model.", + "citation_count": 1, + "topics": [ + "Service and Product Innovation", + "Digital Platforms and Economics" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4237580245", + "title": "Transformative Valuing", + "year": 2020, + "url": "https://doi.org/10.1111/edth.12445", + "doi": "https://doi.org/10.1111/edth.12445", + "abstract": "Abstract In this essay, Randall Curren addresses four basic questions about transformative experiences in education: Is education necessarily transformative? What must education provide for it to be personally transformative? What standard should educators use in determining whether engaging students in potentially transformative experiences is beneficial or justified? How should schools and educators orchestrate opportunities for beneficial transformative experiences? The focus is on transformative valuing, but also on the importance of a fully transformative nexus of personal attributes and social pathways to living a transformed life. Curren argues that educational decisions on behalf of students can be justified on the basis of necessary goods that persist through transformative changes in what is contingently good for them, and that schools' efforts to orchestrate opportunities for transformative experiences should engage students in activities that can be eudaimonic for them.", + "citation_count": 1, + "topics": [ + "Philosophy, Ethics, and Existentialism", + "Ego Development and Educational Practices", + "Critical and Liberation Pedagogy" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2906134827", + "title": "IS TRANSFORMATIVE LEARNING THE ONE THAT PROVIDES TRANSFORMATIVE CAPABILITIES", + "year": 2018, + "url": "https://www.uav.ro/jour/index.php/jpe/article/download/1171/1258", + "doi": null, + "abstract": "The article intents to present, to some extent, if the transformative learning, which has a powerful influence on promoting critical reflection on assumptions and interpretations to engage not just the intellect but affect, identity, worldview, beliefs and values (Mezirow, 2000; Sterling 2011), has certain contributions to students\u2019 transformative capabilities. Transformative capability, related to transformative learning, implies the capacity to learn, innovate and bring about appropriate change and it is connected with the \u201elearning outcomes\u201d, seeded by the Universities in order to be similar with the \u201ecompetences\u201d required by the labour market.", + "citation_count": 1, + "topics": [ + "Social Issues in Poland", + "Appreciative Inquiry and Organizational Change", + "Education and Cultural Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2811109242", + "title": "Travelling Business Models: On adapting business models to new contexts", + "year": 2018, + "url": "https://lup.lub.lu.se/record/06a30e95-801b-4806-95e6-7c5158fd52f1", + "doi": null, + "abstract": "How business models are configured can provide one solution to bringing forward sustainable innovations and transforming businesses and industries so that they become more sustainable. Creating a shift in the energy sector\u2019s use of renewable sources is of particular importance since the energy sector is the source of one-fourth of all global greenhouse gas emissions. In California, the dominant business model for solar energy is the so-called third-party-ownership business model (TPO). It can be described as a \u201cCleantech-as-a-service business model\u201d based on product usage rather than the traditional direct ownership model. The research in this thesis started with the observation that actors in European markets were adopting the TPO business model. However, despite being portrayed in various media and reports as a successful archetype to copy, the TPO was in fact adapted to fit its new market contexts. The research in this thesis focuses on the phenomenon of the \u201ctravelling TPO\u201d to empirically explore how a BM \u201ccirculating out there\u201d as a model to follow is brought into a new context and adapted to fit it. The findings show that business models are highly context dependent and that both external market conditions and internal organizational factors influence business models that are brought into new contexts. The thesis presents business model translation as an emergent and actor-oriented view on how organizations recognize, adopt and adapt business models as models to follow. In line with a translation perspective, the research reveals that the tacit and ambiguous nature of business models allow actors involved in bringing and adapting business models into new settings to shape them according to their experiences and interests. Through interpretations and adaptations, actors continuously create preliminary translations of a business model that iteratively resonate with the new context. This eventually allows the business model to be contextualized in its new setting. (Less)", + "citation_count": 1, + "topics": [ + "Business Strategy and Innovation" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2478288626", + "title": "OpenCL-Accelerated Object Classification in Video Streams using Spatial Pooler of Hierarchical Temporal Memory", + "year": 2017, + "url": "https://doi.org/10.14569/ijacsa.2017.080245", + "doi": "https://doi.org/10.14569/ijacsa.2017.080245", + "abstract": "The paper presents a method to classify objects in video streams using a brain-inspired Hierarchical Temporal Memory (HTM) algorithm. Object classification is a challeng-ing task where humans still significantly outperform machine learning algorithms due to their unique capabilities. A system which achieves very promising performance in terms of recogni-tion accuracy have been implemented. Unfortunately, conducting more advanced experiments is very computationally demanding; some of the trials run on a standard CPU may take as long as several days for 960x540 video streams frames. Therefore, authors decided to accelerate selected parts of the system using OpenCL. In particular, authors seek to determine to what extent porting selected and computationally demanding parts of a core may speed up calculations. The classification accuracy of the system was examined through a series of experiments and the performance was given in terms of F1 score as a function of the number of columns, synapses, min overlap and winners set size. The system achieves the highest F1 score of 0.95 and 0.91 for min overlap=4 and 256 synapses, respectively. Authors have also conduced a series of experiments with different hardware setups and measured CPU/GPU acceleration. The best kernel speed-up of 632x and 207x was reached for 256 synapses and 1024 columns. However, overall acceleration including transfer time was significantly lower and amounted to 6.5x and 3.2x for the same setup.", + "citation_count": 1, + "topics": [ + "Advanced Image and Video Retrieval Techniques", + "Advanced Memory and Neural Computing", + "Neural Networks and Applications" + ], + "discovered_via": [ + "neighbor:openalex:W4391598857" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2563554688", + "title": "The development of the human resources management units", + "year": 2016, + "url": "https://doi.org/10.5937/zrffp46-12475", + "doi": "https://doi.org/10.5937/zrffp46-12475", + "abstract": "The necessity of personnel management became clear in the second half of the 19th century - when its different organizational forms started taking shape. From then, until the mid-80s, the personnel function - personnel management - changes and enriches its contents in accordance with the new reflections on people management in enterprises and companies. During the 1990s, in North America and Europe, the concept of human resource management and 'human resources' as the organizational form of its implementation are increasingly promoted in some organizations. At the same time, personnel departments and the departments of human resources 'coexist' in contemporary practice, while in some socio-cultural settings, there is no basic difference between these two concepts and theoretical levels. Nevertheless, a part of the specialized literature in Europe and in our country, there is a prevailing assessment that the department for Human Resources and Educational Service is an optimal organizational framework for implementation of personnel management - human resources, including a comprehensive guidance on training and development of all categories of employees.", + "citation_count": 1, + "topics": [ + "Employer Branding and e-HRM", + "Regional Development and Management Studies", + "Human Resource and Talent Management" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734", + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2429057255", + "title": "Development of Usability Assessment Indicator of Adjustable Electric Beds for Home Care", + "year": 2016, + "url": "https://doi.org/10.12812/ksms.2016.18.1.45", + "doi": "https://doi.org/10.12812/ksms.2016.18.1.45", + "abstract": "In this study we carry out usability evaluation for adjustable electric beds for home care that are used the most by the elderly, and examines dissatisfaction of users. In this sense, we performed an interview with stakeholders including bed users, developers and suppliers to develop usability evaluation contents. In addition, we carried out feasibility and reliability study over evaluation form through usability assessment, and developed usability evaluation indicator.", + "citation_count": 1, + "topics": [ + "Innovation in Digital Healthcare Systems", + "Cultural and Historical Studies", + "Marine and Coastal Research" + ], + "discovered_via": [ + "neighbor:openalex:W4392182430" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2338849032", + "title": "Internet Transparentnost u Finansijskom Sektoru Bosne i Hercegovine/ Internet Transparency in Financial Sector in Bosnia and Herzegovina", + "year": 2015, + "url": "https://doi.org/10.1515/eoik-2015-0019", + "doi": "https://doi.org/10.1515/eoik-2015-0019", + "abstract": "Rezime Podizanjem kvaliteta korporativnog upravljanja pove\u0107ava se vrijednost preduze\u0107a na tr\u017ei\u0161tu, omogu\u0107uje kori\u0161tenje svih oblika finansiranja: doma\u0107eg i me\u0111unarodnog, javnog i privatnog, a samim tim ja\u010da i njihov dugoro\u010dni prosperitet. Svrha ovog rada je istra\u017eivanje internet transparentnosti u bankama i osiguravaju\u0107im dru\u0161tvima Bosne i Hercegovine i uo\u010davanje razlika izme\u0111u sektora banaka i sektora osiguravaju\u0107ih dru\u0161tava u pogledu stepena transparentnosti pojedinih oblasti iz domena korporativnog upravljanja. Podaci potrebni za istra\u017eivanje su dobijeni pregledanjem internet stranica posmatranih subjekata. Rezultati diskriminacione analize su pokazali da postoji razlika u internet transparentnosti izme\u0111u banaka i osiguravaju\u0107ih dru\u0161tava.", + "citation_count": 1, + "topics": [ + "Taxation and Compliance Studies", + "Microfinance and Financial Inclusion", + "Corporate Finance and Governance" + ], + "discovered_via": [ + "neighbor:openalex:W4318407310" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4230176027", + "title": "UbiComp 2014", + "year": 2015, + "url": "https://doi.org/10.1109/mprv.2015.21", + "doi": "https://doi.org/10.1109/mprv.2015.21", + "abstract": "The 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing represented a wide variety of technologies, methodologies, user scenarios, and institutions. From wearable computing in outer space to smart mats for gyms, contributors pushed the boundaries on what's possible and continued to extend the reach of technology to be ever-more pervasive and ubiquitous.", + "citation_count": 1, + "topics": [ + "IoT and Edge/Fog Computing", + "Augmented Reality Applications" + ], + "discovered_via": [ + "neighbor:openalex:W4390315357" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2994604000", + "title": "University Academics' Behavioural Intention to Use E-Government Services in Sri Lanka", + "year": 2015, + "url": "https://www.iiste.org/Journals/index.php/IKM/article/download/23145/23657", + "doi": null, + "abstract": "Implementation and adoption of Electronic Government services are in early stage in most of the developing economies. Users of such services include individual citizens, businesses, government agencies and other governments. The rewarding benefits of such implementation effort depends both on governments\u2019 end as well as users\u2019 end. This study amended and used Unified Theory of Acceptance and Use of Technology (UTAUT) model to identify the factors influencing university academics\u2019 adoption of electronic government services in Sri Lanka. University teachers from South Eastern University of Sri Lanka were the participants of the study. The real data disclosed that Effort Expectancy and Social Influence factors have significant effect whereas Performance Expectancy factor did not have effect on university academics\u2019 intention to adopt Electronic Government services in Sri Lanka. Keywords : University Academic, Behavioural Intention, Electronic Government, Sri Lanka, UTAUT.", + "citation_count": 1, + "topics": [ + "Technology Adoption and User Behaviour", + "E-Government and Public Services", + "ICT Impact and Policies" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2603787370", + "title": "Transparency in Local Government A Case Study of Transparency in Hamburg and Antwerp: Giving People What They Need?", + "year": 2014, + "url": "https://doi.org/10.26481/marble.2014.v4.200", + "doi": "https://doi.org/10.26481/marble.2014.v4.200", + "abstract": "This paper examines through what means local government can realize transparency policies and to what extent real transparency is achieved. We conduct a comparative study of information policies in Hamburg and Antwerp. In these studies we compare how both cities score on building real transparency in implementing laws aimed at increasing transparency. We define six indicators of real transparency in section 2 Indicators of Real Transparency. The aim of this paper is to show that there can be limits to the transparency effect of information distribution in terms of what is necessary, useful and understandable information transmitted to the public. Ill-considered implementation of right of information and transparency policies is not sufficient. We claim that, the subjective quality of the information, in terms of accessibility but also understandability is of overriding importance.", + "citation_count": 1, + "topics": [ + "E-Government and Public Services", + "Local Government Finance and Decentralization", + "Public Policy and Administration Research" + ], + "discovered_via": [ + "neighbor:openalex:W4404254054" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1193337282", + "title": "Persuasion and the Hygiene of Communication", + "year": 2014, + "url": "https://doi.org/10.18052/www.scipress.com/ilshs.35.86", + "doi": "https://doi.org/10.18052/www.scipress.com/ilshs.35.86", + "abstract": "The study starts from the observation that in everyday life persuasion is inevitable. Under this premise is investigating the negative effects of persuasion through seduction and deceit. To eradicate diseases induced by persuasion would be needed a medicine of communication. As is inevitable, persuasion should be accepted: people have to get used to live with persuasion. As such, it must be taken into account if not eradicate, at least mitigate the negative effects of persuasion. The best tool for such an approach would be \u201chygiene of communication\u201d in which to assess rationally persuasive elements of communication.", + "citation_count": 1, + "topics": [ + "Appreciative Inquiry and Organizational Change", + "Communication Studies and Media" + ], + "discovered_via": [ + "neighbor:openalex:W4407762039" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1503094549", + "title": "Forms of Crowdsourcing", + "year": 2013, + "url": "https://doi.org/10.1002/9781118760765.ch4", + "doi": "https://doi.org/10.1002/9781118760765.ch4", + "abstract": "", + "citation_count": 1, + "topics": [ + "Outsourcing and Supply Chain Management" + ], + "discovered_via": [ + "neighbor:openalex:W4401533174" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2375836089", + "title": "Health and Socioeconomic Status:A Research Based on Multiple Indicators", + "year": 2010, + "url": "http://en.cnki.com.cn/Article_en/CJFDTOTAL-WEIJ201008023.htm", + "doi": null, + "abstract": "Objective:To explore the correlativity between health and socioeconomic status among Chinese residents.Methods:Adopting multiple indicators for health and socioeconomic status in multivariate regressions using pooled data and panel data respectively,and comparing regression results.Results:Generally speaking,residents with higher socioeconomic status have better health.But the correlativity varies when different indicators for health and socioeconomic status are adopted.Conclusion:Socioeconomic status affects health through multiple channels.Therefore,quantitative analysis based on single indicator cannot fully reveal the mechanics between socioeconomic status and health.", + "citation_count": 1, + "topics": [ + "Cardiovascular Health and Risk Factors" + ], + "discovered_via": [ + "neighbor:openalex:W4399518864" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4239295757", + "title": "Special Issue of Computer Communications on Heterogeneous Networking for Quality, Reliability, Security, and Robustness \u2013 Part-II", + "year": 2009, + "url": "https://doi.org/10.1016/j.comcom.2009.06.003", + "doi": "https://doi.org/10.1016/j.comcom.2009.06.003", + "abstract": "", + "citation_count": 1, + "topics": [ + "Energy Efficient Wireless Sensor Networks" + ], + "discovered_via": [ + "neighbor:openalex:W4318407310" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4251085376", + "title": "Two Experiments in Reducing Overconfidence in Spreadsheet Development", + "year": 2008, + "url": "https://doi.org/10.4018/978-1-59904-945-8.ch062", + "doi": "https://doi.org/10.4018/978-1-59904-945-8.ch062", + "abstract": "This paper describes two experiments that examined overconfidence in spreadsheet development. Overconfidence has been seen widely in spreadsheet development and could account for the rarity of testing by end-user spreadsheet developers. The first experiment studied a new way of measuring overconfidence. It demonstrated that overconfidence really is strong among spreadsheet developers. The second experiment attempted to reduce overconfidence by telling subjects in the treatment group the percentage of students who made errors on the task in the past. This warning did reduce overconfidence, and it reduced errors somewhat, although not enough to make spreadsheet development safe.", + "citation_count": 1, + "topics": [ + "Spreadsheets and End-User Computing", + "Statistics Education and Methodologies" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3148130686", + "title": "Socioeconomic Status and Health: Dimensions and Mechanisms", + "year": 2008, + "url": "https://openalex.org/W3148130686", + "doi": null, + "abstract": "This paper reviews the evidence on the well-known positive association between socioeconomic status and health. We focus on four dimensions of socioeconomic status -- education, financial resources, rank, and race and ethnicity -- paying particular attention to how the mechanisms linking health to each of these dimensions diverge and coincide. The extent to which socioeconomic advantage causes good health varies, both across these four dimensions and across the phases of the lifecycle. Circumstances in early life play a crucial role in determining the co-evolution of socioeconomic status and health throughout adulthood. In adulthood, a considerable part of the association runs from health to socioeconomic status, at least in the case of wealth. The diversity of pathways casts doubt upon theories that treat socioeconomic status as a unified concept.", + "citation_count": 1, + "topics": [ + "Health disparities and outcomes", + "Intergenerational and Educational Inequality Studies", + "Employment and Welfare Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4399518864" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2916277247", + "title": "UbiComp 2006 Workshops, Part 2", + "year": 2007, + "url": "https://doi.org/10.1109/mprv.2007.48", + "doi": "https://doi.org/10.1109/mprv.2007.48", + "abstract": "This article presents summaries of five of the UbiComp 2006 Workshops: System Support for Ubiquitous Computing, Pervasive Image Capture and Sharing, Usable Ubiquitous Computing in Next-Generation Conference Rooms, Nurturing Technologies in the Domestic Environment, and UbiHealth. The other summaries appeared in the January--March 2007 issue.", + "citation_count": 1, + "topics": [ + "Context-Aware Activity Recognition Systems" + ], + "discovered_via": [ + "neighbor:openalex:W4390315357" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2593155302", + "title": "K\u016brinio originalumo samprata", + "year": 2005, + "url": "https://ojs.mruni.eu/ojs/jurisprudence/article/download/2941/2744", + "doi": null, + "abstract": "The article deals with one of the features of work \u2013 originality. Using comparative and system analysis methods the conception of originality is examined in two main aspects: originality as criteria to determine whether the work is a result of creative work and originality as criteria to determine if the result of work is not copied from the other work. The first meaning of originality in the states with different law systems is examined: the objective requirement of originality is applied in common law states (the work is original if enough of labour, skill and judgement invested in it); the subjective requirement of originality is applied in continental law states (the work is original if it reflects the personality of the author). The changes in conception of originality in modern works are discussed. The need to protect the new non-material values conditions the declining of requirements for originality. The originality is kept when the work is the result of author\u2019s personal creative activity. The author comes to a conclusion that currently the different requirements of originality for the different kinds of works are applied: the high level of originality is typical for traditional works of art and lower level is characteristic for applied art works and new forms of works (databases, computer programs, photography). The example of case law where the court stated the originality of work, which was created using computer programs, is given. Analysing the second meaning of originality the author searches for the criteria which lets to state the originality of derivative work and the illegal use of work. The problem of protected and non-protected elements of work (the dichotomy of content and form) is discussed, trying to define not protected elements which are not subject of exclusive authors\u2019 rights. The originality of derivative works is examined, saying that the common requirements of originality for them are applied. The article contains review of Lithuanian case law examples: the court stated the violation of copyright because new drawings were exact or similar copy of original ones. The conclusions are given in the end of article.", + "citation_count": 1, + "topics": [ + "Art History and Market Analysis", + "Intellectual Property Law", + "Digital Media and Visual Art" + ], + "discovered_via": [ + "neighbor:openalex:W4381684347" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4211072166", + "title": "Chapters 4-5", + "year": 2003, + "url": "https://doi.org/10.1136/thx.58.suppl_1.i17", + "doi": "https://doi.org/10.1136/thx.58.suppl_1.i17", + "abstract": "These guidelines have been replaced by British Guideline on the Management of Asthma. A national clinical guideline Superseded By 2012 Revision Of 2008 Guideline: British Guideline on the Management of Asthma. Thorax 2003 Feb; 58(Suppl 1): 1\u201394.", + "citation_count": 1, + "topics": [ + "Clinical practice guidelines implementation" + ], + "discovered_via": [ + "neighbor:openalex:W4407173730" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4233871529", + "title": "Chapters 6-7", + "year": 2003, + "url": "https://doi.org/10.1136/thx.58.suppl_1.i32", + "doi": "https://doi.org/10.1136/thx.58.suppl_1.i32", + "abstract": "These guidelines have been replaced by British Guideline on the Management of Asthma. A national clinical guideline Superseded By 2012 Revision Of 2008 Guideline: British Guideline on the Management of Asthma. Thorax 2003 Feb; 58(Suppl 1): 1\u201394.", + "citation_count": 1, + "topics": [ + "Clinical practice guidelines implementation" + ], + "discovered_via": [ + "neighbor:openalex:W4407173730" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2503056598", + "title": "Gender Equity in College Athletics: Exploring Alternatives for Equitably Distributing Participation Opportunities between the Sexes", + "year": 1994, + "url": "https://doi.org/10.1123/jlas.4.2.47", + "doi": "https://doi.org/10.1123/jlas.4.2.47", + "abstract": "", + "citation_count": 1, + "topics": [ + "Sports, Gender, and Society", + "Gender Roles and Identity Studies", + "Sport and Mega-Event Impacts" + ], + "discovered_via": [ + "neighbor:openalex:W4385952870" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4391375266", + "title": "\u0418\u0421\u041f\u041e\u041b\u042c\u0417\u041e\u0412A\u041d\u0418\u0415 \u041f\u041e\u0422\u0415\u041d\u0426\u0418A\u041bA \u0421\u041e\u0426\u0418A\u041b\u042c\u041d\u042b\u0425 \u041fA\u0420\u0422\u041d\u0415\u0420\u041e\u0412 \u0412 \u041f\u041e\u0414\u0413\u041e\u0422\u041e\u0412\u041a\u0415 \u0411\u0423\u0414\u0423\u0429\u0418\u0425 \u041f\u0415\u0414A\u0413\u041e\u0413\u041e\u0412", + "year": 2024, + "url": "https://doi.org/10.51889/2959-5762.2023.80.4.019", + "doi": "https://doi.org/10.51889/2959-5762.2023.80.4.019", + "abstract": "\u0411\u0456\u043b\u0456\u043c \u0431\u0435\u0440y \u049b\u043e\u0493a\u043c\u043d\u044b\u04a3 \u044d\u043a\u043e\u043d\u043e\u043c\u0438\u043aa\u043b\u044b\u049b \u0434a\u043cy\u044b\u043d\u044b\u04a3 \u043d\u0435\u0433\u0456\u0437\u0456, \u04d9\u043b\u0435y\u043c\u0435\u0442\u0442\u0456\u043a \u0442\u04b1\u0440a\u049b\u0442\u044b\u043b\u044b\u049b\u0442\u044b\u04a3 \u0444a\u043a\u0442\u043e\u0440\u043ba\u0440\u044b\u043d\u044b\u04a3 \u0431\u0456\u0440\u0456, \u0445a\u043b\u044b\u049b\u0442\u044b\u04a3 \u0440y\u0445a\u043d\u0438-a\u0434a\u043c\u0433\u0435\u0440\u0448\u0456\u043b\u0456\u043a \u04d9\u043b\u0435y\u0435\u0442\u0456\u043d\u0456\u04a3 \u0436\u04d9\u043d\u0435 \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442ya\u043b\u0434\u044b\u049b \u04e9\u0441y\u0456\u043d\u0456\u04a3 \u049ba\u0439\u043da\u0440 \u043a\u04e9\u0437\u0456 \u0440\u0435\u0442\u0456\u043d\u0434\u0435 \u0431a\u0440\u043b\u044b\u049b ya\u049b\u044b\u0442\u0442a\u0440\u0434a \u0442a\u043f\u0442\u044b\u0440\u043ca\u0441 \u049b\u04b1\u043d\u0434\u044b\u043b\u044b\u049b \u0431\u043e\u043b\u044b\u043f \u0435\u0441\u0435\u043f\u0442\u0435\u043b\u0456\u043f \u043a\u0435\u043b\u0435\u0434\u0456. 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Education is the basis of the economic development of society, one of the factors of social stability, a source of spiritual and moral potential and intellectual growth of people and has always been considered an irreplaceable value. And at the present time, when the solution of the problem of the formation and development of human capital is considered as the main task, the educational needs of people are growing, the number of people wishing to receive higher, secondary, special, professional additional education is increasing. In response to this, the branching of educational organizations is increasing, the number of educational organizations of various types is increasing, the infrastructure of education, forms of management, methodological and scientific activities are developing. Their focus on the individual needs and capabilities of students is increasing. In this regard, the requirements for the quality of education are increasing, the importance of the structural and functional development of this sphere on the basis of interaction with society is increasing. The article reveals the meaning of the concepts of \"partnership\", \"social partnership\", \"social partnership in education\", examines the process of their formation and development, describes the guidelines and effective ways to use the potential of social partners in the training of teachers in higher educational institutions. Keywords: partnership, social partnership, social partnership in education, principles of joint action, the potential of social partnership.", + "citation_count": 0, + "topics": [ + "Human Resources and Workforce", + "Educational Innovations and Challenges", + "Entrepreneurship Studies and Influences" + ], + "discovered_via": [ + "neighbor:openalex:W4392172846", + "neighbor:openalex:W4394590365", + "neighbor:openalex:W4394591980", + "neighbor:openalex:W4399518864", + "neighbor:openalex:W4404228668", + "neighbor:openalex:W4404254054", + "neighbor:openalex:W4407192387", + "neighbor:openalex:W4407762039" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4392895074", + "title": "Inclusion, exclusion, social justice, and children's rights to education. Reflections of a former secondary school inclusion manager", + "year": 2024, + "url": "https://doi.org/10.4324/9781003391562-7", + "doi": "https://doi.org/10.4324/9781003391562-7", + "abstract": "This chapter will examine the complex and, at times, contradictory nature of the role of inclusion manager from the perspective of a former secondary school teacher, later special educational needs co-ordinator (SENCo) and vice principal in a secondary comprehensive school in an urban area of the East Midlands in England. In particular, the chapter, written in the first person of the author, will consider her views in relation to her role alongside established and up-to-date policy and research regarding \u2018inclusion\u2019 in education, in particular with regard to social justice and the reality of practice. The chapter will also consider the issues associated with \u2018inclusion\u2019 and \u2018exclusion\u2019 according to the researcher\u2019s experiences as inclusion manager and explore the rise in the number of school exclusions nationally; possible reasons for this; and, in terms of social justice, the implications of exclusions for children, their family, and local communities. Management strategies used as an alternative to exclusion, such as managed moves (MM), alternative provision (AP), learning support units (LSU), and internal inclusion units (IEU) within secondary state schools in England will also be explored. Further, the chapter will consider the extent to which \u2018inclusion\u2019 has developed into a term applied to children with challenging behaviours, some of whom have special educational needs or disabilities (SEND) or are Looked after Children (LAC), and are perhaps \u2018stuck\u2019 in the revolving door of school exclusion. In terms of specific areas of focus, the chapter will consider the following: Where the notion of inclusion sits, in terms of social justice and children\u2019s rights to an education, including those with SEND, as well as their peers (UNESCO, 2011), and the extent to which one child\u2019s \u2018rights\u2019 might take precedence over those of another child. The relationship between vulnerable groups of learners, including those with SEND, and school exclusion rates, as well as the implications of exclusion for such groups. The role of managed moves (MM), alternative provision (AP), learning support units (LSU), and internal exclusion units (IEU) in reducing school exclusion rates and, thus, enabling children to thrive, or not. The pressures on inclusion managers to take a principled view of how to put the principles of social justice into effect and consider whether it is possible to balance the needs of the child, their parents, the head teacher, the teachers, the school community, and any involved outside agencies. The extent to which an inclusion manager complies with policy: what the reality of the role is in practice and the implications of this. The vital role the inclusion manager plays in terms of teacher and teaching assistant (TA) continuing professional development (CPD). The chapter will be informed by reference to supporting policy and research literature, as well as short student pen portraits to illustrate key themes.", + "citation_count": 0, + "topics": [ + "Education Systems and Policy" + ], + "discovered_via": [ + "neighbor:openalex:W4366989571" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4401768695", + "title": "The Relationship between Personality Advantage and Happiness among College Students: The Moderating Effect of Stressful Life Events", + "year": 2024, + "url": "https://doi.org/10.12677/ap.2024.148572", + "doi": "https://doi.org/10.12677/ap.2024.148572", + "abstract": "", + "citation_count": 0, + "topics": [ + "Education and Learning Interventions", + "Health and Wellbeing Research", + "Healthcare Education and Workforce Issues" + ], + "discovered_via": [ + "neighbor:openalex:W4404228668" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4327743613", + "title": "The saying of Bukhari (He disagrees with his hadith) A critical study in his book The Middle History", + "year": 2023, + "url": "https://doi.org/10.25130/jis.20.11.5.12", + "doi": "https://doi.org/10.25130/jis.20.11.5.12", + "abstract": "Al-Bukhari gives several words to the narrators in his book Al-Tarikh Al-Awsat, including the word: (he contradicts in his hadith), that is, in his hadith he contradicts hafez, and the contradiction among the narrators is no more than two issues. Either the narrator is weak or trustworthy, and if he is weak and contradicts trustworthy or Hafiz, then his hadith is objectionable, and if he is trustworthy and disagreed with the trustworthy, or who is more trustworthy than him, then his hadith is odd. Al-Bukhari has given this word to seven narrators. The current study shows that all of them are poor and they have contradicted the more trustworthy hafiz.", + "citation_count": 0, + "topics": [ + "Medieval and Classical Philosophy", + "Turkish Literature and Culture", + "Islamic Studies and History" + ], + "discovered_via": [ + "neighbor:openalex:W4381848566" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4387497383", + "title": "\u0424\u041e\u0420\u041c\u0418\u0420\u041e\u0412A\u041d\u0418\u0415 \u0413\u041e\u0422\u041e\u0412\u041d\u041e\u0421\u0422\u0418 \u0411\u0423\u0414\u0423\u0429\u0418\u0425 \u041f\u0415\u0414A\u0413\u041e\u0413\u041e\u0412 \u041a \u041e\u0420\u0413A\u041d\u0418\u0417A\u0426\u0418\u0418 \u0420A\u0411\u041e\u0422\u042b \u041f\u041e \u0420A\u0417\u0412\u0418\u0422\u0418\u042e \u0412A\u041b\u0415\u041e\u041b\u041e\u0413\u0418\u0427\u0415\u0421\u041a\u041e\u0419 \u041a\u0423\u041b\u042c\u0422\u0423\u0420\u042b \u0428\u041a\u041e\u041b\u042c\u041d\u0418\u041a\u041e\u0412", + "year": 2023, + "url": "https://doi.org/10.51889/2077-6861.2023.1.30.016", + "doi": "https://doi.org/10.51889/2077-6861.2023.1.30.016", + "abstract": "\u041ca\u049ba\u043ba\u0434a a\u0432\u0442\u043e\u0440\u043ba\u0440 \u04e9\u0441\u043a\u0435\u043b\u0435\u04a3 \u04b1\u0440\u043fa\u049b \u04af\u0448i\u043d \u0441a\u043ba\u0443a\u0442\u0442\u044b \u04e9\u043ci\u0440 \u0441a\u043b\u0442\u044b\u043d \u049ba\u043b\u044b\u043f\u0442a\u0441\u0442\u044b\u0440\u0443 \u0431\u04af\u0433i\u043d\u0434\u0435 \u043c\u0435\u043a\u0442\u0435\u043f\u0442\u0435\u0440 \u043c\u0435\u043d \u0436\u043e\u0493a\u0440\u044b \u043e\u049b\u0443 \u043e\u0440\u044b\u043d\u0434a\u0440\u044b\u043d\u044b\u04a3 \u0442\u04d9\u0436i\u0440\u0438\u0431\u0435\u0441i\u043d\u0434\u0435 \u0431i\u043bi\u043c \u0431\u0435\u0440\u0443\u0434i i\u0437\u0433i\u043b\u0435\u043d\u0434i\u0440\u0443\u0434i\u04a3 \u0431a\u0441\u044b\u043c \u0431a\u0493\u044b\u0442\u0442a\u0440\u044b\u043d\u044b\u04a3 \u0431i\u0440i \u0440\u0435\u0442i\u043d\u0434\u0435 \u049ba\u0440a\u0441\u0442\u044b\u0440\u044b\u043ba\u0434\u044b \u0434\u0435\u043f \u0441a\u043da\u0439\u0434\u044b. \u00ab\u0412a\u043b\u0435\u043e\u043b\u043e\u0433\u0438\u044f\u043b\u044b\u049b \u043c\u04d9\u0434\u0435\u043d\u0438\u0435\u0442\u00bb \u04b1\u0493\u044b\u043c\u044b\u043da \u0435\u0440\u0435\u043a\u0448\u0435 \u043c\u04d9\u0440\u0442\u0435\u0431\u0435 \u0431\u0435\u0440i\u043b\u0435\u0434i, \u043e\u043d\u044b\u04a3 \u049ba\u043b\u044b\u043f\u0442a\u0441\u0443\u044b\u043d\u044b\u04a3 \u043ca\u04a3\u044b\u0437\u0434\u044b\u043b\u044b\u0493\u044b \u0431a\u0441\u0442a\u0443\u044b\u0448 \u043c\u0435\u043a\u0442\u0435\u043f \u043e\u049b\u0443\u0448\u044b\u043ba\u0440\u044b \u043c\u044b\u0441a\u043b\u044b \u0440\u0435\u0442i\u043d\u0434\u0435 \u043a\u04e9\u0440\u0441\u0435\u0442\u043a\u0435\u043d\u0434\u0435\u0439, \u043e\u043ba\u0440\u0434\u044b\u04a3 \u0444\u0438\u0437\u0438\u043aa\u043b\u044b\u049b, \u043f\u0441\u0438\u0445\u0438\u043aa\u043b\u044b\u049b \u0436\u04d9\u043d\u0435 \u04d9\u043b\u0435\u0443\u043c\u0435\u0442\u0442i\u043a \u04d9\u043b-a\u0443\u049ba\u0442\u044b\u043d \u0441a\u049b\u0442a\u0443 \u043c\u0435\u043d \u0436a\u049b\u0441a\u0440\u0442\u0443 \u049ba\u0436\u0435\u0442\u0442i\u043bi\u0433i\u043d\u0435\u043d \u0442\u0443\u044b\u043d\u0434a\u0439\u0434\u044b. 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A\u0432\u0442\u043e\u0440\u044b \u0443\u0442\u0432\u0435\u0440\u0436\u0434a\u044e\u0442, \u0447\u0442\u043e \u0437\u0434\u043e\u0440\u043e\u0432\u044c\u0435 \u0438 \u0444\u0438\u0437\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0441\u043f\u043e\u0441\u043e\u0431\u043d\u043e\u0441\u0442\u0438 \u044f\u0432\u043b\u044f\u044e\u0442\u0441\u044f \u0443\u0441\u043b\u043e\u0432\u0438\u0435\u043c \u0438 \u043e\u0441\u043d\u043e\u0432\u043e\u0439 \u0440a\u0441\u043a\u0440\u044b\u0442\u0438\u044f \u043f\u043e\u0442\u0435\u043d\u0446\u0438a\u043ba \u0447\u0435\u043b\u043e\u0432\u0435\u043aa. \u041e\u0442 \u0442\u043e\u0433\u043e, \u043da\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0443\u0441\u043f\u0435\u0448\u043d\u043e \u043c\u043e\u0436\u043d\u043e \u0444\u043e\u0440\u043c\u0438\u0440\u043e\u0432a\u0442\u044c \u0438 \u0443\u043a\u0440\u0435\u043f\u043b\u044f\u0442\u044c \u0432\u043f\u043e\u0441\u043b\u0435\u0434\u0441\u0442\u0432\u0438\u0438 \u043e\u0441\u043d\u043e\u0432\u044b \u0432a\u043b\u0435\u043e\u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u043a\u0443\u043b\u044c\u0442\u0443\u0440\u044b \u0438 \u043da\u0432\u044b\u043a\u0438 \u0437\u0434\u043e\u0440\u043e\u0432\u043e\u0433\u043e \u043e\u0431\u0440a\u0437a \u0436\u0438\u0437\u043d\u0438 \u0432 \u043c\u043e\u043b\u043e\u0434\u043e\u043c \u0432\u043e\u0437\u0440a\u0441\u0442\u0435, \u0437a\u0432\u0438\u0441\u0438\u0442 \u0440\u0435a\u043b\u044c\u043d\u044b\u0439 \u043e\u0431\u0440a\u0437 \u0436\u0438\u0437\u043d\u0438 \u0447\u0435\u043b\u043e\u0432\u0435\u043aa. \u0412 \u0441\u0432\u044f\u0437\u0438 \u0441 \u044d\u0442\u0438\u043c \u0432 \u0432\u0443\u0437a\u0445 \u0441\u0447\u0438\u0442a\u044e\u0442 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u043c \u0441\u0434\u0435\u043ba\u0442\u044c a\u043a\u0446\u0435\u043d\u0442 \u043da \u0444\u043e\u0440\u043c\u0438\u0440\u043e\u0432a\u043d\u0438\u0438 \u0433\u043e\u0442\u043e\u0432\u043d\u043e\u0441\u0442\u0438 \u0431\u0443\u0434\u0443\u0449\u0438\u0445 \u0443\u0447\u0438\u0442\u0435\u043b\u0435\u0439 \u043a \u043e\u0440\u0433a\u043d\u0438\u0437a\u0446\u0438\u0438 \u0440a\u0431\u043e\u0442\u044b \u043f\u043e \u0440a\u0437\u0432\u0438\u0442\u0438\u044e \u0432a\u043b\u0435\u043e\u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u043a\u0443\u043b\u044c\u0442\u0443\u0440\u044b \u0448\u043a\u043e\u043b\u044c\u043d\u0438\u043a\u043e\u0432. A\u0432\u0442\u043e\u0440\u044b \u043f\u0440\u0435\u0434\u043ba\u0433a\u044e\u0442 \u0440\u044f\u0434 \u0432a\u0436\u043d\u044b\u0445 \u0438\u0434\u0435\u0439 \u0432 \u044d\u0442\u043e\u043c \u043da\u043f\u0440a\u0432\u043b\u0435\u043d\u0438\u0438, \u0434a\u044e\u0442 \u0445a\u0440a\u043a\u0442\u0435\u0440\u0438\u0441\u0442\u0438\u043a\u0443 \u043c\u0435\u0442\u043e\u0434\u0443 \u0440a\u0431\u043e\u0442\u044b. \u041a\u043b\u044e\u0447\u0435\u0432\u044b\u0435 \u0441\u043b\u043e\u0432a: \u0437\u0434\u043e\u0440\u043e\u0432\u044c\u0435, \u0437\u0434\u043e\u0440\u043e\u0432\u044b\u0439 \u043e\u0431\u0440a\u0437 \u0436\u0438\u0437\u043d\u0438, \u0432a\u043b\u0435\u043e\u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043aa\u044f \u043a\u0443\u043b\u044c\u0442\u0443\u0440a, \u0444\u043e\u0440\u043c\u0438\u0440\u043e\u0432a\u043d\u0438\u0435, \u043f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u043e\u043da\u043b\u044c\u043da\u044f \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043aa. In the article, the authors believe that the establishment of a healthy lifestyle for the younger generation is today considered in the practice of schools and universities as one of the priority directions of the humanization of education. A special status is given to the concept of \u201cvaleological culture\u201d, the significance of the formation of which, as exemplified by younger schoolchildren, is due to the need to preserve and improve their physical, mental and social well-being. The authors state that health and physical capacity are a condition and basis for the disclosure of a person\u2019s potential. The real lifestyle of a person subsequently depends on how successfully it is possible to form and consolidate the foundations of valeological culture and the skills of a healthy lifestyle at a young age. In this regard, it is believed that in universities it is necessary to pay special attention to the formation of the readiness of future teachers to organize work to develop the valeological culture of schoolchildren. The authors offer a number of important ideas in this direction, give a description of the method of work. Keywords: health, healthy lifestyle, valeological culture, formation of readiness, professional training", + "citation_count": 0, + "topics": [ + "Educational Methods and Analysis", + "Digital Storytelling and Education", + "Educator Training and Historical Pedagogy" + ], + "discovered_via": [ + "neighbor:openalex:W4362655490" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4386774980", + "title": "Pursuing 2030 Sustainable Development Agenda Through Gender Equality: Synergies of Gender Diversity and Financial Performance in Brazilian Publicly Traded Companies", + "year": 2023, + "url": "https://doi.org/10.1007/978-3-031-34436-7_3", + "doi": "https://doi.org/10.1007/978-3-031-34436-7_3", + "abstract": "", + "citation_count": 0, + "topics": [ + "Gender Diversity and Inequality", + "Corporate Finance and Governance", + "Family Business Performance and Succession" + ], + "discovered_via": [ + "neighbor:openalex:W4322742241" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4388292429", + "title": "Methods for Implementing Usable Secure Online Public Services", + "year": 2023, + "url": "https://doi.org/10.1007/978-3-031-43383-2_5", + "doi": "https://doi.org/10.1007/978-3-031-43383-2_5", + "abstract": "", + "citation_count": 0, + "topics": [ + "Privacy, Security, and Data Protection", + "E-Government and Public Services", + "Privacy-Preserving Technologies in Data" + ], + "discovered_via": [ + "neighbor:openalex:W4392182430" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4286665796", + "title": "Designing Prototypes for Inclusion", + "year": 2022, + "url": "https://doi.org/10.5465/ambpp.2022.11027abstract", + "doi": "https://doi.org/10.5465/ambpp.2022.11027abstract", + "abstract": "Nearly every organization is now striving for more diversity and inclusion for all. However, few achieve this. Current theorizing on these topics focuses on the psychological experience of inclusion and associated organizational practices. In contrast, we offer a theoretical perspective focused on understanding how and when organzations will possess the capacity for inclusion by looking at the cognitive foundations of people\u2019s ability to accept and embrace difference. This paper argues that one major barrier to inclusive diversity is a restrictively defined member prototype \u2013 the set of features that define what it means to be a member of the organization or relevant collective. Although organizations often want a well-defined prototype to set standards and norms for employees, this also leads to the exclusion or marginalization of all who do not readily fit this standard. We discuss how the configuration of prototypes determines inclusiveness \u2013 the capacity for inclusive diversity. In doing so, we offer scholars and organizations a new way to anticipate when diversity and inclusion initiatives may fail (i.e., when prototype inclusiveness is low, other well-intentioned efforts will fall short), and reveal novel strategies likely to increase inclusion. We identify four dimensions of prototype configuration and discuss their relationship for inclusiveness. We also offer a research agenda for testing the novel implications of our model and for exploring further considerations and consequences of prototypes in diversity and inclusion scholarship.", + "citation_count": 0, + "topics": [ + "Gender Diversity and Inequality", + "Environmental Sustainability in Business", + "Corporate Social Responsibility Reporting" + ], + "discovered_via": [ + "neighbor:openalex:W4366989571" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3214759741", + "title": "The 2014 Evaluation of China\u2019s Government Transparency", + "year": 2021, + "url": "https://doi.org/10.1007/978-981-16-5032-1_8", + "doi": "https://doi.org/10.1007/978-981-16-5032-1_8", + "abstract": "", + "citation_count": 0, + "topics": [ + "E-Government and Public Services" + ], + "discovered_via": [ + "neighbor:openalex:W4386714740" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3204984834", + "title": "A Fundamental Study of Artificial Intelligence and Its Legal Implications in India", + "year": 2021, + "url": "https://thedesignengineering.com/index.php/DE/article/view/5455", + "doi": null, + "abstract": ": The ability of machines to execute cognitive tasks like thinking, perceiving, learning, problem solving and decision making, is termed as Artificial Intelligence (AI). AI has evolved in ways that far exceed its original conception, and initially it is conceived as a technology that could mine human intelligence. With the help of incredible advances that are made in data collection, processing and computation power, intelligent systems can now be able to deploy to perform a variety of tasks, enable connectivity and improve efficiency. As we have its utility in a growing number of fields due to dramatic increment in AI\u2019s capability. India future lies in sixth of the world\u2019s population. Artificial Intelligence (AI) role is shaping in India\u2019s development and growth is bound to be substantial as its revolution sweeps through societies and enters in our daily life. Despite of the fact that AI provide mechanisms to leapfrog traditional hurdles such as poor infrastructure and bureaucracy but for India, AI holds promise as catalyst to accelerate progress. In emerging technologies, superior research capabilities have been the cornerstone of leadership aspirations and effectively realizing the growth potential which requires expertise in both core and applied research. Despite indications of recent positive efforts in this aspect of technology, in India AI research is still considered infancy and need large scale concerted and combined interventions. In this paper, with various Indian perspectives of AI we also describe fundamentals of Artificial intelligence, various opportunities and challenges for AI in India and also the work provides the brief scenario about the legal perspective of the AI in India.", + "citation_count": 0, + "topics": [ + "COVID-19 Digital Contact Tracing" + ], + "discovered_via": [ + "neighbor:openalex:W2992233710" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3197551843", + "title": "THE ROLE OF BUSINESS MODEL FROM ITS INTERPRETATION FOR ENTREPRENEURSHIP: A REVIEW", + "year": 2021, + "url": "https://tijarijournal.com/index.php/home/article/view/21", + "doi": null, + "abstract": "The business model definition is no longer defined as how an entrepreneur generates revenues or attracts clients. An in-depth understanding of the role of business models is required to understand the role of business models in running a business. This article aims to understand the interpretation of the business model and the business model role of entrepreneurship. This article explores the business model\u2019s role; a comprehensive literature review was used by identifying, critically evaluating, and integrating the findings of all relevant literature review of the business model definition. The results reveal that the business model is about value creation, value capture, engaging customer, customer satisfaction, deliver value, competitive advantage, and opportunity identification. This article shows that the business model establishes how the entrepreneurs should start and run the business and gain a competitive advantage in increasing the market share. The findings point to how the interpretation of the business model provides a better understanding of an entrepreneur to start or implement the business model to start or operate the businesses. This article offers a conceptualisation of how the theory corresponds with business practice and provides benefits for entrepreneurs to understand better how to start or operate their businesses.", + "citation_count": 0, + "topics": [ + "Entrepreneurship Studies and Influences" + ], + "discovered_via": [ + "neighbor:openalex:W4385558068" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3187384844", + "title": "Gender Bias in Equity Crowdfunding", + "year": 2021, + "url": "http://eprints.bournemouth.ac.uk/35823/", + "doi": null, + "abstract": "This research considers if equity investors on crowdfunding platforms displays bias against females. We argue that an increased number of females in a team is associated with a decreased level of funding success, which we test using a dataset of 397 campaigns. From this study we conclude that the aggregated gender within the language used in the investors comments has an influence upon the success of the funding obtained from equity investors. We demonstrate that physical gender of the team, gender of the primary signatory, and any gendered nuances in the language within the campaign, can influence the final decision.", + "citation_count": 0, + "topics": [ + "FinTech, Crowdfunding, Digital Finance", + "Microfinance and Financial Inclusion" + ], + "discovered_via": [ + "neighbor:openalex:W4385952870" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3140300071", + "title": "Research Guides: Using Scopus to Support Your Research: Introduction to Scopus", + "year": 2021, + "url": "https://guides.library.unk.edu/usingscopus", + "doi": null, + "abstract": "This guide is intended mainly for faculty at UNK, but can also be utilized by students for assignments and in their work on research projects. This page will introduce Scopus as a tool in the Scholarly Communications landscape.", + "citation_count": 0, + "topics": [ + "scientometrics and bibliometrics research" + ], + "discovered_via": [ + "neighbor:openalex:W4316038168" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4210604288", + "title": "\u041f\u0443\u0431\u043b\u0456\u043a\u0430\u0446\u0456\u044f \u043d\u0430\u0443\u043a\u043e\u0432\u0438\u0445 \u0441\u0442\u0430\u0442\u0435\u0439 \u0443 \u043d\u0430\u0443\u043a\u043e\u043c\u0435\u0442\u0440\u0438\u0447\u043d\u0438\u0445 \u0431\u0430\u0437\u0430\u0445 Scopus \u0442\u0430 Web of Science", + "year": 2021, + "url": "https://doi.org/10.31219/osf.io/eyvus", + "doi": "https://doi.org/10.31219/osf.io/eyvus", + "abstract": "\u0423 \u0437\u0432\u2018\u044f\u0437\u043a\u0443 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"openalex:W3005856188", + "title": "Trustworthy AI.", + "year": 2020, + "url": "http://arxiv.org/pdf/2002.06276.pdf", + "doi": null, + "abstract": "The promise of AI is huge. AI systems have already achieved good enough performance to be in our streets and in our homes. However, they can be brittle and unfair. For society to reap the benefits of AI systems, society needs to be able to trust them. Inspired by decades of progress in trustworthy computing, we suggest what trustworthy properties would be desired of AI systems. By enumerating a set of new research questions, we explore one approach--formal verification--for ensuring trust in AI. Trustworthy AI ups the ante on both trustworthy computing and formal methods.", + "citation_count": 0, + "topics": [ + "Adversarial Robustness in Machine Learning", + "Ethics and Social Impacts of AI", + "Privacy-Preserving Technologies in Data" + ], + "discovered_via": [ + "neighbor:openalex:W4381848566" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3113185420", + "title": "A Case Study of Transformative Learning of College English Teachers", + "year": 2020, + "url": "https://doi.org/10.1007/978-981-33-4572-0_154", + "doi": "https://doi.org/10.1007/978-981-33-4572-0_154", + "abstract": "", + "citation_count": 0, + "topics": [ + "Adult and Continuing Education Topics", + "Global Educational Policies and Reforms", + "Critical and Liberation Pedagogy" + ], + "discovered_via": [ + "neighbor:openalex:W4391974599" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3113483414", + "title": "Digital Transformation Business Landscape", + "year": 2020, + "url": "https://doi.org/10.1007/978-3-030-60049-5_3", + "doi": "https://doi.org/10.1007/978-3-030-60049-5_3", + "abstract": "", + "citation_count": 0, + "topics": [ + "Information Technology Governance and Strategy", + "Innovation and Knowledge Management", + "Big Data and Business Intelligence" + ], + "discovered_via": [ + "neighbor:openalex:W4391652653" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2965447900", + "title": "Towards Preparation for the Trustworthiness Certification using Trustworthiness Cases", + "year": 2019, + "url": "https://doi.org/10.1007/978-3-658-27488-7_13", + "doi": "https://doi.org/10.1007/978-3-658-27488-7_13", + "abstract": "", + "citation_count": 0, + "topics": [ + "Information and Cyber Security", + "Information Technology Governance and Strategy", + "Software Engineering Techniques and Practices" + ], + "discovered_via": [ + "neighbor:openalex:W4381848566" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3037873431", + "title": "Strategija upravljanja ljudskim potencijalima na promjeru Kaufland K. D.", + "year": 2019, + "url": "https://dabar.srce.hr/en/islandora/object/vusb%3A883", + "doi": null, + "abstract": "The paper deals with the topic of human resources management, for example Kufland\r\n k.d. At the beginning of the work it describes what human resources are, and what their importance\r\n is for each organization. The second paper deals with the human resources management strategy\r\n in business organizations, human resource management as the basis for achieving and retaining\r\n competitive advantage. The elements of human resources management are described in detail, as\r\n well as the stress and impact of stress on employee's work ability. Furthermore, one entire chapter\r\n of this paper is based on the research of talent and their importance and how they are collected and\r\n retained.\r\n The task was to analyze the human resources management strategy in the Kaufland organization.\r\n Theoretical analysis was carried out in co-operation with the Kaufland k.d. human resources\r\n department and a concluding opinion on their work and efforts was made, especially in the Human\r\n Resources Department.\r\n Theoretical analysis showed that Kaufland k.d. an excellent example of quality management of\r\n human resources and talents for the winners of the Employer Partner award for three years in a\r\n row.\r\n The results of the analysis indicate that all organizations should copy their mode of operation and\r\n that they can be an example in human resource management.", + "citation_count": 0, + "topics": [ + "Regional Development and Management Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734", + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2965417904", + "title": "Which Kind of Employees Benefits More from Gender Diversity?", + "year": 2019, + "url": "https://doi.org/10.5465/ambpp.2019.12784abstract", + "doi": "https://doi.org/10.5465/ambpp.2019.12784abstract", + "abstract": "Extant research has shown that gender-diverse organizations tend to be more innovative than gender homogenous ones but has left unaddressed the question of who, among an organization\u2019s workforce, becomes more innovative as gender diversity increases. We address this question by examining how gender diversity affects individual-level innovative performance, measured through patent-based indicators, within the R&D labs of the 40 largest pharmaceutical companies over the period 1985-2010. We argue that higher levels of gender diversity increase the performance of three categories of scientists \u2013 women, rookies, and brokers \u2013 whereas men, long-tenured scientists, and scientists embedded in constrained networks experience limited or no performance benefits. By demonstrating that gender diversity has a heterogeneous effect across different segments of an organization\u2019s workforce, these results both deepen and qualify current understandings of how gender diversity affects innovation within organizations. A key implication of our proposed argument is that gender diversity does not only affect the overall performance of the organization; it also affects how performance (and hence the material and symbolic resources associated with it) are distributed within the organization.", + "citation_count": 0, + "topics": [ + "Gender Diversity and Inequality" + ], + "discovered_via": [ + "neighbor:openalex:W4322742241" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2899084033", + "title": "DETERMINING QUALITY REQUIREMENTS AT THE UNIVERSITIES TO IMPROVE THE QUALITY OF EDUCATION", + "year": 2018, + "url": "https://doi.org/10.26577/be-2018-2-2141", + "doi": "https://doi.org/10.26577/be-2018-2-2141", + "abstract": "\u0411\u04b1\u043b 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\u049ba\u0442\u044b\u0441\u0442\u044b\u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0442\u0435\u0440\u0434\u0456\u04a3\u049ba\u0436\u0435\u0442\u0442\u0456\u043b\u0456\u043a\u0442\u0435\u0440\u0456\u043da\u043d\u044b\u049b\u0442a\u0443, \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0442\u0435\u0440\u0434\u0456\u04a3\u049ba\u043da\u0493a\u0442\u0442a\u043d\u0443, \u049ba\u043da\u0493a\u0442\u0442a\u043d\u0431a\u0443 \u0434\u04d9\u0440\u0435\u0436\u0435\u043b\u0435\u0440\u0456\u043d\u0431\u0435\u043b\u0433\u0456\u043b\u0435\u0443,\u0431\u0456\u043b\u0456\u043c\u0431\u0435\u0440\u0443\u0441a\u043fa\u0441\u044b\u043d a\u043d\u044b\u049b\u0442a\u0443 \u043c\u0435\u043d \u0436\u0435\u0442\u0456\u043b\u0434\u0456\u0440\u0443\u0436\u043e\u043b\u0434a\u0440\u044b\u043d \u0442a\u043b\u0434a\u0443 \u0431\u043e\u043b\u044b\u043f\u0442a\u0431\u044b\u043ba\u0434\u044b. \u041e\u0441\u044b \u043ca\u049b\u0441a\u0442\u049ba\u0436\u0435\u0442\u0443\u04af\u0448\u0456\u043d, \u0435\u04a3 a\u043b\u0434\u044b\u043c\u0435\u043d\u041aa\u043d\u043e \u0441a\u0443a\u043b\u043da\u043ca\u0441\u044b \u0442\u04af\u0437\u0456\u043b\u0456\u043f,116 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u043a\u0435\u049b\u043e\u043b\u0434a\u043d\u044b\u043b\u0434\u044b\u0436\u04d9\u043d\u0435\u0431\u0456\u043b\u0456\u043c\u0431\u0435\u0440\u0443\u0433\u0435\u0436\u04d9\u043d\u0435\u043e\u043d\u044b\u04a3 \u0441a\u043fa\u0441\u044b\u043da \u049ba\u0442\u044b\u0441\u0442\u044b\u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0442\u0435\u0440\u0434\u0456\u04a3\u0442a\u043ba\u043f\u0442a\u0440\u044b \u043c\u0435\u043d \u049ba\u0436\u0435\u0442\u0442\u0456\u043b\u0456\u043a\u0442\u0435\u0440\u0456\u0442\u043e\u043f\u0442\u044b\u049b\u0436\u04b1\u043c\u044b\u0441\u0442a\u0440a\u0440\u049b\u044b\u043b\u044ba\u043d\u044b\u049b\u0442a\u043b\u0434\u044b. \u0415\u043a\u0456\u043d\u0448\u0456\u0434\u0435\u043d,\u0431\u04b1\u043b a\u043d\u044b\u049b\u0442a\u043b\u0493a\u043d \u0442a\u043ba\u043f\u0442a\u0440 \u043c\u0435\u043d \u049ba\u0436\u0435\u0442\u0442\u0456\u043b\u0456\u043a\u0442\u0435\u0440\u041aa\u043d\u043e \u0431a\u0493a\u043ba\u0443 \u043a\u0435\u0441\u0442\u0435\u0441\u0456\u043c\u0435\u043d\u0436\u0456\u043a\u0442\u0435\u043b\u0434\u0456.\u041e\u0441\u044b\u043ba\u0439\u0448a, \u0441a\u043fa \u0442a\u043ba\u043f\u0442a\u0440\u044b \u0442\u04e9\u0440\u0442 \u0441a\u043da\u0442\u049ba \u0431\u04e9\u043b\u0456\u043d\u0434\u0456:\u0431\u043e\u043b\u0443\u044b\u0442\u0438\u0456\u0441, \u0431\u0456\u0440 \u04e9\u043b\u0448\u0435\u043c\u0434\u0456,\u0442a\u0440\u0442\u044b\u043c\u0434\u044b\u0436\u04d9\u043d\u0435\u0431\u0435\u0439\u0442a\u0440a\u043f.\u0421\u043e\u04a3\u044b\u043d\u0434a,\u049ba\u043da\u0493a\u0442\u0442a\u043d\u0443 \u043c\u0435\u043d \u049ba\u043da\u0493a\u0442\u0442a\u043d\u0431a\u0443\u0434\u044b\u04a3 \u043c\u04d9\u043d\u0434\u0435\u0440\u0456\u0435\u0441\u0435\u043f\u0442\u0435\u043b\u0434\u0456\u0436\u04d9\u043d\u0435\u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0442\u0435\u0440\u0434\u0456\u04a3\u049ba\u043da\u0493a\u0442\u0442a\u043d\u0443 \u043c\u0435\u043d \u049ba\u043da\u0493a\u0442\u0442a\u043d\u0431a\u0443 \u0434\u0435\u04a3\u0433\u0435\u0439\u043b\u0435\u0440\u0456\u043d\u0436\u043e\u0493a\u0440\u044b\u043ba\u0442\u0443 \u043c\u0435\u043d \u0442\u04e9\u043c\u0435\u043d\u0434\u0435\u0442\u0443\u0434\u0435\u043e\u0441\u044b\u0442a\u043ba\u043f\u0442a\u0440 \u043c\u0435\u043d \u049ba\u0436\u0435\u0442\u0442\u0456\u043b\u0456\u043a\u0442\u0435\u0440\u0434\u0456\u04a3\u0440\u04e9\u043b\u0456a\u0439\u049b\u044b\u043d a\u043d\u044b\u049b\u0442a\u043b\u0434\u044b.\u0422\u04af\u0439\u0456\u043d\u0441\u04e9\u0437\u0434\u0435\u0440:\u0441a\u043fa, \u0441a\u043fa\u043b\u044b\u049b \u049ba\u0436\u0435\u0442\u0442\u0456\u043b\u0456\u043a\u0442\u0435\u0440,\u0431\u0456\u043b\u0456\u043c\u0431\u0435\u0440\u0443\u0441a\u043fa\u0441\u044b, \u041aa\u043d\u043e \u043c\u043e\u0434\u0435\u043b\u0456.", + "citation_count": 0, + "topics": [ + "Public Administration and Governance", + "Educational Leadership and Administration", + "Education Practices and Challenges" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331", + "neighbor:openalex:W4318407310", + "neighbor:openalex:W4392172846", + "neighbor:openalex:W4399518864", + "neighbor:openalex:W4404228668", + "neighbor:openalex:W4404254054", + "neighbor:openalex:W4407192387" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2800270373", + "title": "Caracterizaci\u00f3n del proceso inclusi\u00f3n/exclusi\u00f3n en j\u00f3venes universitarios de la Carrera de Pedagog\u00eda", + "year": 2018, + "url": "https://dspace.ups.edu.ec/bitstream/123456789/15431/1/UPS-QT12591.pdf", + "doi": null, + "abstract": "The present case study is about the different kinds of inclusion and exclusion that exist within the Career of Pedagogy at the Salesian Polytechnic University (U.P.S), therefore the goal of this case study is to recognize and identify the inclusion and exclusion practices within the University.\r\nThis work hopes to respond to the following question: Which inclusion and exclusion practices do the Pedagogy students at the Salesian Polytechnic University engage in? The process allowed for a fieldwork where qualitative research was executed using the interview method, thus creating a dialogue that allowed to recognize how students perceive the subject in question in regards to their experiences at the university. It is important to mention that during the research process several interviews were likely applied to the students and to the professors at the Salesian Polytechnic University (Giron).\r\nThe dominant results obtained from the case study were exclusion based on the socio and laboral structure, the Exclusion / Inclusion by Mass Media, Exclusion based on Culture, Exclusion based on fashion, Exclusion based on career choice or major, Intercultural Inclusion, and Socioeconomic Inclusion. On the other hand, you can also observe the interactions between the students and teachers at U.P.S, which demonstrate Inclusion / Exclusion based on Sexual Preferences and the Inclusion of People with disabilities among other tendencies.", + "citation_count": 0, + "topics": [ + "Inclusive Education and Diversity" + ], + "discovered_via": [ + "neighbor:openalex:W4366989571" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2948807893", + "title": "\u041a\u043e\u043c\u043c\u0443\u043d\u0438\u043aa\u0442\u0438\u0432\u043d\u043e- \u043f\u0440a\u0433\u043ca\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 a\u043da\u043b\u0438\u0437 \u0434\u0438\u043f\u043b\u043e\u043ca\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0431\u0443\u043ca\u0433 (\u043da \u043e\u0441\u043d\u043e\u0432\u0435 \u0432\u0435\u0440\u0431a\u043b\u044c\u043d\u044b\u0445 \u043d\u043e\u0442)", + "year": 2018, + "url": "https://doaj.org/article/0525bb7b8d3748d1b4c96c613194f141", + "doi": null, + "abstract": "The article is devoted to communicative and pragmatic features of diplomatic papers. In this article, the analysis of the communicative and pragmatic specifics of Arab diplomatic papers was carried out for the first time, aimed at revealing their specific integrants. The study is conducted through consideration of lexical and syntactic problems of diplomatic correspondence. As a result of the research, it was possible to show for the first time the reality of the communicative-pragmatic specifics and the structural differentiation of the functional-communicative syntax of Arab diplomatic papers caused by it. The pragmatics of informing the genre of a verbal note finds its expression in the definition of the space-time relationship (chronotope) of participants in the diplomatic discourse (addressee and addressee) and the object under discussion. It is proved that the verb plays a basic role in the indication of the time of diplomatic discourse. In addition to verbs, accurate time indicators are actively used in the texts of verbal notes, such as indicating the number, month and year, which indicates the institutional nature of the genre. The indication of exact dates (date - date, day of the week, month, year) reflects a prospective character; interaction of communicants. In this paper, an attempt was made to analyze the linguopragmatic characteristics of diplomatic discourse from the perspective of pragmalinguistics and discursive analysis. This study is a step towards further research of the discursive genre of written diplomatic communication as a promising subject of linguistic analysis.", + "citation_count": 0, + "topics": [ + "Linguistic research and analysis", + "Discourse Analysis and Cultural Communication", + "Lexicography and Language Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4362655490" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3173606202", + "title": "FEATURES AND DIFFERENCES OF ADEQUATE AND EQUIVALENT TRANSLATION", + "year": 2018, + "url": "https://doaj.org/article/d33502c47f5747ab9ad8d266e0425c3a", + "doi": null, + "abstract": "\u049aa\u0439 \u0443a\u049b\u044b\u0442\u0442a \u0431\u043e\u043b\u043ca\u0441\u044b\u043d \u043c\u04d9\u0434\u0435\u043d\u0438\u0435\u0442a\u0440a\u043b\u044b\u049b \u049ba\u0440\u044b\u043c-\u049ba\u0442\u044b\u043da\u0441\u0442a\u0440\u0434\u044b\u04a3 \u0436a\u049b\u0441\u044b \u0434\u0435\u04a3\u0433\u0435\u0439\u0434\u0435 \u0436\u04af\u0437\u0435\u0433\u0435 a\u0441\u0443\u044b \u043d\u0435 \u049b\u04b1\u043b\u0434\u044b\u0440a\u0443\u044b \u0431a\u0441\u0442a\u043f\u049b\u044b \u043c\u04d9\u0442\u0456\u043d\u043d\u0456\u04a3 \u0431a\u0441\u049ba \u0442\u0456\u043b\u0434\u0435\u0433\u0456 a\u0443\u0434a\u0440\u043ca\u0441\u044b\u043c\u0435\u043d a\u0434\u0435\u043a\u0432a\u0442\u0442\u044b \u043d\u0435 \u0431a\u043ba\u043ca\u043b\u044b \u0431\u043e\u043b\u0443\u044b\u043da \u0442\u0456\u043a\u0435\u043b\u0435\u0439 \u0431a\u0439\u043ba\u043d\u044b\u0441\u0442\u044b. \u041e\u0441\u044b\u0493a\u043d \u043e\u0440a\u0439, \u043a\u04e9\u043f\u0442\u0435\u0433\u0435\u043d \u0493a\u043b\u044b\u043c\u0434a\u0440 a\u0434\u0435\u043a\u0432a\u0442\u0442\u044b\u043b\u044b\u049b \u043f\u0435\u043d \u0431a\u043ba\u043ca\u043b\u044b\u043b\u044b\u049b 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\u04d9\u0440\u049ba\u0448a\u043d a\u0443\u0434a\u0440\u043ca\u043d\u044b\u04a3 \u043a\u043e\u043d\u0432\u0435\u043d\u0446\u0438\u043e\u043da\u043b\u0434\u044b \u043d\u043e\u0440\u043ca\u0442\u0438\u0432\u0442\u0456 \u0442a\u043ba\u043f\u0442a\u0440\u044b\u043da \u0441\u04d9\u043a\u0435\u0441 \u0431\u043e\u043ba \u0431\u0435\u0440\u043c\u0435\u0439\u0434\u0456.", + "citation_count": 0, + "topics": [ + "Families in Therapy and Culture", + "Islamic Thought and Society Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4362655490" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2886410948", + "title": "Library Co-creation via Crowdsourcing", + "year": 2018, + "url": "https://pulinet.oas.psu.ac.th/index.php/journal/article/download/320/321", + "doi": null, + "abstract": "\u0e2a\u0e33\u0e19\u0e01\u0e2b\u0e2d\u0e2a\u0e21\u0e14 \u0e21\u0e2b\u0e32\u0e27\u0e17\u0e22\u0e32\u0e25\u0e22\u0e02\u0e2d\u0e19\u0e41\u0e01\u0e19 \u0e43\u0e0a\u0e40\u0e04\u0e23\u0e2d\u0e07\u0e21\u0e2d Crowdsourcing \u0e43\u0e19\u0e01\u0e32\u0e23\u0e23\u0e1a\u0e1f\u0e07\u0e40\u0e2a\u0e22\u0e07\u0e1c\u0e43\u0e0a\u0e1a\u0e23\u0e01\u0e32\u0e23 \u0e2b\u0e25\u0e01\u0e01\u0e32\u0e23\u0e02\u0e2d\u0e07 Crowdsourcing \u0e04\u0e2d\u0e41\u0e17\u0e19\u0e17\u0e08\u0e30\u0e21\u0e2d\u0e1a\u0e43\u0e2b\u0e1a\u0e04\u0e04\u0e25\u0e2b\u0e23\u0e2d\u0e1d\u0e32\u0e22\u0e43\u0e14\u0e1d\u0e32\u0e22\u0e2b\u0e19\u0e07\u0e17\u0e33\u0e01\u0e32\u0e23\u0e1e\u0e12\u0e19\u0e32\u0e2d\u0e30\u0e44\u0e23\u0e43\u0e2b\u0e21\u0e02\u0e19\u0e21\u0e32 \u0e2b\u0e23\u0e2d\u0e17\u0e33\u0e01\u0e32\u0e23\u0e15\u0e14\u0e2a\u0e19\u0e43\u0e08 \u0e41\u0e01\u0e44\u0e02\u0e1b\u0e0d\u0e2b\u0e32\u0e40\u0e23\u0e2d\u0e07\u0e43\u0e14\u0e40\u0e23\u0e2d\u0e07\u0e2b\u0e19\u0e07 \u0e01\u0e25\u0e1a\u0e40\u0e1b\u0e14\u0e42\u0e2d\u0e01\u0e32\u0e2a\u0e43\u0e2b\u0e1d\u0e07\u0e0a\u0e19\u0e2b\u0e23\u0e2d\u0e2a\u0e32\u0e18\u0e32\u0e23\u0e13\u0e0a\u0e19\u0e17\u0e27\u0e44\u0e1b\u0e44\u0e14\u0e21\u0e42\u0e2d\u0e01\u0e32\u0e2a\u0e43\u0e19\u0e01\u0e32\u0e23\u0e04\u0e14\u0e01\u0e32\u0e23\u0e15\u0e14\u0e2a\u0e19\u0e43\u0e08\u0e41\u0e17\u0e19 \u0e2a\u0e33\u0e19\u0e01\u0e2b\u0e2d\u0e2a\u0e21\u0e14\u0e08\u0e07\u0e19\u0e33\u0e41\u0e19\u0e27\u0e04\u0e14\u0e19\u0e21\u0e32\u0e40\u0e1e\u0e2d\u0e43\u0e2b\u0e1c\u0e43\u0e0a\u0e1a\u0e23\u0e01\u0e32\u0e23\u0e21\u0e2a\u0e27\u0e19\u0e23\u0e27\u0e21\u0e2a\u0e23\u0e32\u0e07\u0e2a\u0e23\u0e23\u0e04 Content \u0e2b\u0e23\u0e2d\u0e17\u0e40\u0e23\u0e22\u0e01\u0e27\u0e32 Co-Creation \u0e40\u0e1b\u0e19\u0e01\u0e25\u0e22\u0e17\u0e18\u0e17\u0e32\u0e07\u0e01\u0e32\u0e23\u0e15\u0e25\u0e32\u0e14\u0e17\u0e40\u0e19\u0e19\u0e01\u0e32\u0e23\u0e1b\u0e0f\u0e2a\u0e21\u0e1e\u0e19\u0e18 (Interaction) \u0e40\u0e19\u0e19\u0e01\u0e32\u0e23\u0e15\u0e14\u0e15\u0e2d\u0e2a\u0e2d\u0e2a\u0e32\u0e23\u0e40\u0e1e\u0e2d\u0e41\u0e25\u0e01\u0e40\u0e1b\u0e25\u0e22\u0e19\u0e04\u0e27\u0e32\u0e21\u0e04\u0e14 \u0e04\u0e27\u0e32\u0e21\u0e15\u0e2d\u0e07\u0e01\u0e32\u0e23 \u0e04\u0e27\u0e32\u0e21\u0e04\u0e14\u0e40\u0e2b\u0e19\u0e23\u0e30\u0e2b\u0e27\u0e32\u0e07\u0e01\u0e19 \u0e40\u0e1e\u0e2d\u0e17\u0e33\u0e43\u0e2b\u0e40\u0e01\u0e14\u0e04\u0e13\u0e04\u0e32\u0e40\u0e1e\u0e21\u0e43\u0e19\u0e23\u0e1b\u0e41\u0e1a\u0e1a\u0e02\u0e2d\u0e07\u0e2a\u0e19\u0e04\u0e32 \u0e1a\u0e23\u0e01\u0e32\u0e23 \u0e2b\u0e23\u0e2d\u0e1b\u0e23\u0e30\u0e2a\u0e1a\u0e01\u0e32\u0e23\u0e13\u0e23\u0e27\u0e21 \u0e14\u0e27\u0e22\u0e01\u0e32\u0e23\u0e19\u0e33\u0e41\u0e19\u0e27\u0e04\u0e14 Framework for Building a Co-creation Capability \u0e21\u0e32\u0e14\u0e33\u0e40\u0e19\u0e19\u0e01\u0e32\u0e23\u0e44\u0e14\u0e41\u0e01 1. \u0e01\u0e32\u0e23\u0e23\u0e1a\u0e1f\u0e07 (Listen) \u0e14\u0e27\u0e22\u0e01\u0e32\u0e23\u0e43\u0e0a Crowdsourcing 2. \u0e04\u0e27\u0e32\u0e21\u0e1c\u0e01\u0e1e\u0e19 (Engage) \u0e14\u0e27\u0e22\u0e01\u0e32\u0e23\u0e43\u0e2b\u0e1c\u0e43\u0e0a\u0e21\u0e2a\u0e27\u0e19\u0e23\u0e27\u0e21\u0e43\u0e19\u0e01\u0e32\u0e23\u0e2d\u0e2d\u0e01\u0e41\u0e1a\u0e1a\u0e1a\u0e23\u0e01\u0e32\u0e23 3. \u0e01\u0e32\u0e23\u0e15\u0e2d\u0e1a\u0e2a\u0e19\u0e2d\u0e07 (Respond) \u0e40\u0e1e\u0e2d\u0e2a\u0e23\u0e32\u0e07\u0e2a\u0e23\u0e23\u0e04\u0e1a\u0e23\u0e01\u0e32\u0e23\u0e43\u0e2b\u0e15\u0e23\u0e07\u0e01\u0e1a\u0e04\u0e27\u0e32\u0e21\u0e15\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e02\u0e2d\u0e07\u0e1c\u0e43\u0e0a\u0e1a\u0e23\u0e01\u0e32\u0e23\u0e41\u0e25\u0e30\u0e40\u0e01\u0e14\u0e01\u0e32\u0e23\u0e21\u0e2a\u0e27\u0e19\u0e23\u0e27\u0e21\u0e02\u0e2d\u0e07\u0e1c\u0e43\u0e0a\u0e1a\u0e23\u0e01\u0e32\u0e23\u0e2d\u0e22\u0e32\u0e07\u0e41\u0e17\u0e08\u0e23\u0e07 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\u0e0b\u0e07\u0e40\u0e1b\u0e19\u0e17\u0e2a\u0e19\u0e43\u0e08\u0e02\u0e2d\u0e07\u0e04\u0e13\u0e30\u0e1c\u0e28\u0e01\u0e29\u0e32\u0e14\u0e07\u0e32\u0e19\u0e08\u0e32\u0e01\u0e2b\u0e25\u0e32\u0e22\u0e41\u0e2b\u0e07\u0e17\u0e43\u0e2b\u0e04\u0e27\u0e32\u0e21\u0e2a\u0e19\u0e43\u0e08\u0e43\u0e19\u0e23\u0e1b\u0e41\u0e1a\u0e1a\u0e02\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e19\u0e33 Crowdsourcing \u0e21\u0e32\u0e43\u0e0a\u0e07\u0e32\u0e19\u0e17\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e19\u0e33\u0e44\u0e1b\u0e1b\u0e23\u0e1a\u0e43\u0e0a\u0e43\u0e19\u0e07\u0e32\u0e19\u0e2b\u0e2d\u0e07\u0e2a\u0e21\u0e14\u0e44\u0e14\u0e07\u0e32\u0e22\u0e14\u0e32\u0e22 \u0e42\u0e14\u0e22\u0e44\u0e21\u0e21\u0e04\u0e32\u0e43\u0e0a\u0e08\u0e32\u0e22 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"abstract": "The nationally-recognized Susquehanna\r\nChorale will delight audiences of all\r\nages with a diverse mix of classic and\r\ncontemporary pieces. The ChoraleA\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u00a2A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0080A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0099s\r\nperformances have been described\r\nas A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u00a2A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0080A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u009cemotionally unfiltered, honest\r\nmusic making, successful in their\r\naim to make the audience feel,\r\nto be moved, to be part of the\r\nperformance - and all this while\r\nworking at an extremely high\r\nmusical level.A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u00a2A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0080A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0083A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0083A\u0083\u00c2\u0082A\u0082\u00c2\u0082A\u0083\u00c2\u0083A\u0082\u00c2\u0082A\u0083\u00c2\u0082A\u0082\u00c2\u009d Experience choral\r\nsinging that will take you to new\r\nheights!", + "citation_count": 0, + "topics": [ + "Theater, Performance, and Music History", + "Music Education and Analysis", + "Music History and Culture" + ], + "discovered_via": [ + "neighbor:openalex:W4318406331", + "neighbor:openalex:W4318407310", + "neighbor:openalex:W4321105716", + "neighbor:openalex:W4391598857", + "neighbor:openalex:W4392172846", + "neighbor:openalex:W4394590365", + "neighbor:openalex:W4394591980", + "neighbor:openalex:W4399518864", + "neighbor:openalex:W4404228668", + "neighbor:openalex:W4404254054", + "neighbor:openalex:W4407192387", + "neighbor:openalex:W4407762039" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2758277628", + "title": "\"Kindertransport,\" Oct. 5, 6, and 7, 8:00 p.m.", + "year": 2017, + "url": "https://www.messiah.edu/events/event/879/kindertransport", + "doi": null, + "abstract": 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by: Diane Samuels\r\nDirected by: Clair Myers\r\nA modern classic about one 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struggle to come to terms with her past 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brutally separated from her German Jewish parents at the age of 9 and brought to England with the promise of a new life. This play examines the life, during World War II and afterwards, of a kindertransport child. Though fictitious, it is based upon many real kindertransport stories.\r\nClimenhaga Building, Poorman Black Box Theater\r\nOct. 5-7, 8 p.m.; Oct. 8, 3 p.m.", + "citation_count": 0, + "topics": [ + "Ecology, Conservation, and Geographical Studies", + "German Literature and Culture Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4362655490" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3183948672", + "title": "\u00e0\u00a4\u0161\u00e0\u00a4\u00bf\u00e0\u00a4\u00a4\u00e0\u00a4\u00b2\u00e0\u00a4\u00b5\u00e0\u00a4\u00be\u00e0\u00a4\u00a8\u00e0\u00a4\u00be \u00e0\u00a4\u00aa\u00e0\u00a4\u201a\u00e0\u00a4\u0161\u00e0\u00a4\u00be\u00e0\u00a4\u00af\u00e0\u00a4\u00a4 \u00e0\u00a4\u00b8\u00e0\u00a4\u00ae\u00e0\u00a4\u00bf\u00e0\u00a4\u00a4\u00e0\u00a4\u00bf \u00e0\u00a4\u00ae\u00e0\u00a5\u2021\u00e0\u00a4\u201a \u00e0\u00a4\u00ae\u00e0\u00a4\u00be\u00e0\u00a4\u00a8\u00e0\u00a4\u00b5 \u00e0\u00a4\u2014\u00e0\u00a4\u00b0\u00e0\u00a5\u20ac\u00e0\u00a4\u00ac\u00e0\u00a5\u20ac \u00e0\u00a4\u00b8\u00e0\u00a5\u201a\u00e0\u00a4\u0161\u00e0\u00a4\u2022\u00e0\u00a4\u00be\u00e0\u00a4\u201a\u00e0\u00a4\u2022 - 2016 ( \u00e0\u00a4\u00e0\u00a4\u2022 \u00e0\u00a4\u2014\u00e0\u00a5\u00e0\u00a4\u00b0\u00e0\u00a4\u00be\u00e0\u00a4\u00ae \u00e0\u00a4\u00b8\u00e0\u00a5\u00e0\u00a4\u00a4\u00e0\u00a4\u00b0\u00e0\u00a5\u20ac\u00e0\u00a4\u00af \u00e0\u00a4\u00e0\u00a5\u0152\u00e0\u00a4\u2014\u00e0\u00a5\u2039\u00e0\u00a4\u00b2\u00e0\u00a4\u00bf\u00e0\u00a4\u2022 \u00e0\u00a4\u2026\u00e0\u00a4\u00a7\u00e0\u00a5\u00e0\u00a4\u00af\u00e0\u00a4\u00af\u00e0\u00a4\u00a8 )", + "year": 2017, + "url": "http://irjms.in/index.php/files/article/view/477", + "doi": null, + "abstract": "a\u00a4\u00b0a\u00a4\u00bea\u00a4\u0153a\u00a4\u00b8a\u00a5\u008da\u00a4\u00a5a\u00a4\u00bea\u00a4\u00a8 a\u00a4\u2022a\u00a5\u2021 a\u00a4aa\u00a4\u00b6a\u00a5\u008da\u00a4sa\u00a4?a\u00a4\u00aea\u00a5\u20ac a\u00a4\u00ada\u00a4\u00bea\u00a4\u2014 a\u00a4\u00aea\u00a5\u2021a\u00a4\u201a a\u00a4\u00b8a\u00a5\u008da\u00a4\u00a5a\u00a4?a\u00a4\u00a4 a\u00a4\u00aea\u00a4\u00b0a\u00a5\u201aa\u00a4\u00b8a\u00a5\u008da\u00a4\u00a5a\u00a4\u00b2a\u00a5\u20aca\u00a4\u00af a\u00a4\u0153a\u00a4?a\u00a4\u00b2a\u00a5\u2021 a\u00a4\u00b8a\u00a4\u00bea\u00a4\u00aea\u00a4\u00bea\u00a4\u0153a\u00a4?a\u00a4\u2022 - a\u00a4\u2020a\u00a4\u00b0a\u00a5\u008da\u00a4\u00a5a\u00a4?a\u00a4\u2022 a\u00a4\u00b5a\u00a4?a\u00a4\u2022a\u00a4\u00bea\u00a4\u00b8 a\u00a4\u2022a\u00a5\u20ac a\u00a4\u00a6a\u00a5\u0192a\u00a4\u00b7a\u00a5\u008da\u00a4\u0178a\u00a4? a\u00a4\u00b8a\u00a5\u2021 a\u00a4aa\u00a4?a\u00a4\u203aa\u00a4ia\u00a4\u00bca\u00a5\u2021 a\u00a4\u00b9a\u00a5\u0081a\u00a4\u008f a\u00a4\u00b9a\u00a5\u02c6a\u00a4\u201aa\u00a5\u00a4 a\u00a4\u2026a\u00a4\u00a4a\u00a4\u0192 a\u00a4\u2021a\u00a4\u00a8 a\u00a4\u0153a\u00a4?a\u00a4\u00b2a\u00a5\u2039a\u00a4\u201a a\u00a4\u00aea\u00a5\u2021a\u00a4\u201a a\u00a4\u00b0a\u00a4\u00b9a\u00a4\u00a8a\u00a5\u2021 a\u00a4\u00b5a\u00a4\u00bea\u00a4\u00b2a\u00a5\u2021 a\u00a4\u00b2a\u00a5\u2039a\u00a4\u2014a\u00a5\u2039a\u00a4\u201a a\u00a4\u00aea\u00a5\u2021a\u00a4\u201a a\u00a4\u2014a\u00a4\u00b0a\u00a5\u20aca\u00a4\u00aca\u00a5\u20ac a\u00a4\u2022a\u00a4\u00be a\u00a4\u00b8a\u00a5\u008da\u00a4\u00a4a\u00a4\u00b0 a\u00a4\u2030a\u00a4sa\u00a5\u008da\u00a4s a\u00a4aa\u00a4\u00bea\u00a4\u00afa\u00a4\u00be a\u00a4\u0153a\u00a4\u00bea\u00a4\u00a4a\u00a4\u00be a\u00a4\u00b9a\u00a5\u02c6a\u00a5\u00a4 a\u00a4\u00b5a\u00a4?a\u00a4\u00b6a\u00a5\u2021a\u00a4\u00b7 a\u00a4\u00b0a\u00a5\u201aa\u00a4a a\u00a4\u00b8a\u00a5\u2021 a\u00a4\u2014a\u00a5\u008da\u00a4\u00b0a\u00a4\u00bea\u00a4\u00aea\u00a5\u20aca\u00a4\u00a3 a\u00a4\u2022a\u00a5\u008da\u00a4\u00b7a\u00a5\u2021a\u00a4\u00a4a\u00a5\u008da\u00a4\u00b0a\u00a5\u2039a\u00a4\u201a a\u00a4\u00aea\u00a5\u2021a\u00a4\u201a a\u00a4\u0153a\u00a4\u00b9a\u00a4\u00bea\u00a4\u201a a\u00a4\u2022a\u00a5\u0192a\u00a4\u00b7a\u00a4? 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It is the basis of their taxation, as well as the most frequently used conversion factor for calculating the value of built-up land and premises. Unfortunately, the current legal regulations on usable floor area records remain inconsistent and imprecise. This paper is an attempt to identify the main problems related to the determination of usable floor area of building structures and their parts for various purposes. The definitions of usable floor area contained in the relevant legislation will be compared, with particular regard to the provisions on the cadastre and real property taxation. The authors emphasise the consequences of the existing differences and imperfections of these definitions and discuss the criteria for classifying specific space as usable floor area.", + "citation_count": 0, + "topics": [ + "3D Modeling in Geospatial Applications" + ], + "discovered_via": [ + "neighbor:openalex:W4392182430" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2760605468", + "title": "Vybran\u00e9 probl\u00e9my person\u00e1lneho riadenia v hoteloch a re\u0161taur\u00e1ci\u00e1ch", + "year": 2016, + "url": "https://www.theses.cz/id/fe5gp5/", + "doi": null, + "abstract": "Thesis deals with the topic of human resources management in a selected restaurant. The aim is to provide information on human resources management as well as human resources activities, involved in ascertaining the role of human resources work and human resources management, to make an analysis of human resources activities within the selected company and to make proposals on improvement of certain human resources activities. Author first defines human resources management, human resources actions, human resources particularities and specificities of human resources activities in small and medium-sized companies, such as scheduling needs of workers, staff evaluation, deployment, rewarding, training, labor relations, employee benefits, layoffs and retirements. Follow-up work contains characteristics of selected restaurants, an analysis of selected human resources activities, and questionnaire survey and research results of the author. Finally at the end of the work submitted proposals for the improvement of certain human resources activities.", + "citation_count": 0, + "topics": [ + "Education, Psychology, and Social Research", + "Employer Branding and e-HRM" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734", + "neighbor:openalex:W4382520149" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2552566180", + "title": "LibGuides: SCOPUS' metrics: determining a journal's performance: Home", + "year": 2016, + "url": "https://liu.brooklyn.libguides.com/c.php?g=463956", + "doi": null, + "abstract": "LIU has subscriptions to Journal Citation Reports and SCOPUS. While JCR's Impact Factor is more widely known, SCOPUS includes more citations making it possible one will find the journal they are searching for indexed in SCOPUS and not in JCR.", + "citation_count": 0, + "topics": [ + "scientometrics and bibliometrics research", + "Academic Publishing and Open Access" + ], + "discovered_via": [ + "neighbor:openalex:W4316038168" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4233273178", + "title": "Message from the general chair", + "year": 2016, + "url": "https://doi.org/10.1109/mercon.2016.7480099", + "doi": "https://doi.org/10.1109/mercon.2016.7480099", + "abstract": "Since last year (2015) MERCon publications are visible in the SCOPUS database/index. This is a significant achievement because SCOPUS is increasingly being used by many agencies to judge the research performance of both individuals and institutions. Both the THES (Times Higher Education Supplement) and QS (Quacquarelli Symonds) university ranking systems use SCOPUS. For this reason the University of Moratuwa is moving towards treating SCOPUS as a benchmark for quality publications. So MERCon's inclusion in SCOPUS is indeed a big step towards its international credibility; it will also probably attract progressively better quality papers in years to come, including overseas ones.", + "citation_count": 0, + "topics": [ + "Advances in Oncology and Radiotherapy", + "Healthcare Quality and Management", + "Innovations in Medical Education" + ], + "discovered_via": [ + "neighbor:openalex:W4316038168" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2261122642", + "title": "\ubaa8\ubc14\uc77c\ub7ec\ub2dd \uc11c\ube44\uc2a4\uc758 \uc0ac\uc6a9\uc790 \uc218\uc6a9\uc758\ub3c4\uc5d0 \uad00\ud55c \uc2e4\uc99d\uc5f0\uad6c", + "year": 2015, + "url": "http://www.dbpia.co.kr/Article/NODE06366425", + "doi": null, + "abstract": "The purpose of this study is to find the user\u2019s acceptance intention of mobile learning services based on the UTAUT(Unified Theory of Acceptance and Use of Technology) model and self-efficacy theory. This study utilized a SEM(Structural Equation Model) for the hypothesis testing. A total of 328 responses were used for analysis and the research results are as follow. The research results show that self-efficacy affects performance expectancy(PE), effect expectancy(EE), social influence(SI), and behavioral Intention. Also, performance expectancy(PE) affects behavioral intention. On the other side, effort expectancy(EE) affects performance expectancy(PE) and behavioral intention. Finally, social influence affects effort expectance(EE) and behavioral intention.", + "citation_count": 0, + "topics": [ + "Technology Adoption and User Behaviour", + "Education and Learning Interventions" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2337920774", + "title": "How to Increase the Accuracy of Crowdsourcing Campaigns", + "year": 2015, + "url": "https://openalex.org/W2337920774", + "doi": null, + "abstract": "Crowdsourcing is a new approach to performing tasks, with a group of volunteers rather than experts. For example, the Geo-Wiki project [1] aims to improve the global land-cover map by crowdsourcing for image recognition. Though crowdsourcing gives a simple way to perform tasks that are hard to automate, analysis of data received from non-experts is a challenging problem that requires a holistic approach. Here we study in detail the dataset of the Cropland Capture game (part of Geo-Wiki project) to increase the accuracy of campaign\u2019s results. Using this analysis, we developed a methodology for a generic type of crowdsourcing campaign similar to the Cropland Capture game. The proposed methodology relies on computer vision and machine learning techniques. Using the Cropland Capture dataset we showed that our methodology increases agreement between aggregated volunteers\u2019 votes and experts\u2019 decisions from 77% to 86%. \\n \\n[1] Fritz, Steffen, et al. \u201cGeo-Wiki. Org: The use of crowdsourcing to improve global land cover.\u201d Remote Sensing 1.3 (2009): 345-354.", + "citation_count": 0, + "topics": [ + "Species Distribution and Climate Change", + "Data-Driven Disease Surveillance", + "Geographic Information Systems Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4401533174" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2326236677", + "title": "Prospects of Development of Personnel Capacity of the Udmurt Republic", + "year": 2014, + "url": "https://doi.org/10.12737/4881", + "doi": "https://doi.org/10.12737/4881", + "abstract": "The paper presents such concepts, as \u00abpotential capacity\u00bb, \u00abpersonnel/workforce\u00bb, \u00abworkforce capacity\u00bb. Statistics are provided on unemployment rate at the territory of Udmurt Republic as of the beginning of 2014. The author proposes recommendations on further reducing the numbers of unemployed and outlines specialties in demand, considers, how workforce capacity is formed and used; how inner and outer conditions, under which workforce capacity is developed, are formed; describes ways to enhance effectiveness of workforce capacity use; considers the important attributes to be taken into account when forming workforce capacity, the way to assess present workforce capacity. The author also proposes a number of regional-level problems to be solved in coming years and a set of activities, conducive to improving and developing workforce capacity of Udmurt Republic.", + "citation_count": 0, + "topics": [ + "Regional Economics and Spatial Analysis", + "Economic Growth and Productivity", + "Regional Development and Policy" + ], + "discovered_via": [ + "neighbor:openalex:W4402876502" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2392455911", + "title": "Discussion on how to shape garden Zen realm", + "year": 2013, + "url": "http://en.cnki.com.cn/Article_en/CJFDTOTAL-JZSX201303113.htm", + "doi": null, + "abstract": "To begin with the shaping method of garden Zen realm,the paper describes the definition of garden Zen realm space,artificial shaping method of Garden Zen realm,and shaping method of garden Zen realm,and studies modern garden Zen realm shaping method,which has provided some service for higher Zen realm space design in future.", + "citation_count": 0, + "topics": [ + "Color perception and design" + ], + "discovered_via": [ + "neighbor:openalex:W4383913712" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1591794021", + "title": "A Look at Global Workforce by Year 2020 & ways to Manage it", + "year": 2013, + "url": "https://www.indianjournals.com/ijor.aspx?target=ijor:aca&volume=3&issue=8&article=015", + "doi": null, + "abstract": "The paper attempts to study and examine the past and present global workforce and based on it tries to anticipate the trend of corporate workforce in 2020. It looks at the pattern in India, as against other countries. The endeavor is to map the paradigm evolvement of workforce from the typical 9 to 5 set-up to that of work-from-home set up and its pros and cons. It also looks at the demographics of workforce, the role of women workforce and the entry of younger generation. It traces upon the challenges and strengths of the workforce and based on statistics and experts views tries to recommend ways of managing and motivating it.", + "citation_count": 0, + "topics": [ + "Employment and Welfare Studies", + "Indian Economic and Social Development", + "Employee Welfare and Language Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4402876502" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3004686567", + "title": "A good man but a bad wizard. About the limits and future of transparency of democratic governments", + "year": 2012, + "url": "https://doi.org/10.5555/2656990.2656998", + "doi": "https://doi.org/10.5555/2656990.2656998", + "abstract": "Computer-mediated transparency is seen as a powerful tool to attain policy goals and to transform government. This is based on the idea that transparency is something good in itself, which can be a...", + "citation_count": 0, + "topics": [ + "E-Government and Public Services" + ], + "discovered_via": [ + "neighbor:openalex:W4386714740", + "neighbor:openalex:W4404254054" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W1573400912", + "title": "Corporate Governance and the Protection of Stakeholder Interests in British Banks", + "year": 2012, + "url": "https://doi.org/10.54648/eucl2012005", + "doi": "https://doi.org/10.54648/eucl2012005", + "abstract": "This paper explores how stakeholder ideas can be applied to a discussion of the corporate governance of banks and suggests that a much broader range of stakeholders need to be considered when thinking about bank governance.", + "citation_count": 0, + "topics": [ + "Corporate Insolvency and Governance", + "Corporate Governance and Law", + "Global Financial Regulation and Crises" + ], + "discovered_via": [ + "neighbor:openalex:W4400303336" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W4237784285", + "title": "Generative Grammar", + "year": 2012, + "url": "https://doi.org/10.1002/9781405198431.wbeal0449", + "doi": "https://doi.org/10.1002/9781405198431.wbeal0449", + "abstract": "Abstract Generative grammar is an approach to linguistics which is usually associated with Noam Chomsky and his followers, starting in the mid\u20101950s and continuing to the present day. Generative grammar has had a number of different names through its development.", + "citation_count": 0, + "topics": [ + "Multilingual Education and Policy", + "Language and cultural evolution", + "Syntax, Semantics, Linguistic Variation" + ], + "discovered_via": [ + "neighbor:openalex:W4391830676" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2374248756", + "title": "Transfixion of Feng Youlan's Utilitarian Realm and Ethical Realm\u2014\u2014A Discuss with Mr Chen Xiaoping", + "year": 2011, + "url": "http://en.cnki.com.cn/Article_en/CJFDTOTAL-LGXB201101012.htm", + "doi": null, + "abstract": "The essential difference between utilitarian realm and ethical realm lies in outlook on justice and interest and outlook on individual and group,but this does not interfere with transfixion between utilitarian realm and ethical realm.From outlook on justice and interest,justice and interest are the unity of opposites, and the possibility of the transfixion between the two realms isfor whose interest.From outlook on individual and group,although different even opposed ideas in the two realms exist,but this does not affect the ascension of realm of life from utilitarian realm to ethical realm.The process to ascense realm of life is essentially a process of individual socialization.", + "citation_count": 0, + "topics": [ + "Chinese history and philosophy" + ], + "discovered_via": [ + "neighbor:openalex:W4383913712" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W90316445", + "title": "What the students expect and perceive about trustworthiness", + "year": 2011, + "url": "https://openalex.org/W90316445", + "doi": null, + "abstract": "It is a simple study that trying to examine the gap between the students' expectation on trustworthiness and their perceived trustworthiness. The study formulated four research questions. The implications and recommendation for future research were also presented.", + "citation_count": 0, + "topics": [ + "Emotional Intelligence and Performance" + ], + "discovered_via": [ + "neighbor:openalex:W4381848566" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2365451219", + "title": "Effects of the Guideline of Community-aquired Pneumonia on the Achievement Ratio of CAP Therapy", + "year": 2011, + "url": "https://en.cnki.com.cn/Article_en/CJFDTOTAL-KMYX201109026.htm", + "doi": null, + "abstract": "Objective To analyze the effects of the guideline of community-aquired pneumonia on the achievement ratio of CAP therapy.Methods The data of 417 community-aquired pneumonia patients was analyzed retrospectively.The effect of choosing the first dose antibiotics and CAP guideline on the achievement ratio of CAP therapy was explored.Results(1)The percentage of patients choosing the first dose antibiotic and following Chinese CAP guideline was 68.8%.There was no significant difference in the percentage in five years.(2)There was a statistically significant difference in the failure rate of CAP therapy between the patients who abided by guideline to select antibiotics and the patients who did not abide by guideline(\u03c72=20.458,P0.001).Conclusion The guideline can decrease the failure rate of firstly selecting antibiotics.", + "citation_count": 0, + "topics": [ + "Infectious Diseases and Tuberculosis", + "Pneumonia and Respiratory Infections", + "Tuberculosis Research and Epidemiology" + ], + "discovered_via": [ + "neighbor:openalex:W4407173730" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; 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topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2386000789", + "title": "A Contrastive Study between Generative Grammar and Cognitive Grammar", + "year": 2011, + "url": "http://en.cnki.com.cn/Article_en/CJFDTOTAL-LQSZ201103026.htm", + "doi": null, + "abstract": "Generative Grammar exerts deep influence on the appearance and development of Cognitive Grammar,but they differ greatly from each other.After exploring the different philosophical foundations between generative grammar and Cognitive Grammar,this article makes a contrast of their features.In the end,the article points out that whether it is Generative Grammar or Cognitive Grammar,they aim at disclosing the relationship between human cognition and language,paving the way for further exploring the secret of human language and cognition.", + "citation_count": 0, + "topics": [ + "Language, Metaphor, and Cognition" + ], + "discovered_via": [ + "neighbor:openalex:W4391830676" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2046296964", + "title": "The policy challenges of universally usable e-government", + "year": 2011, + "url": "https://doi.org/10.1145/2037556.2037627", + "doi": "https://doi.org/10.1145/2037556.2037627", + "abstract": "This proposal describes a panel on the topic of universally usable e-government for the 2011 Digital Government conference. Many e-government services are not universally usable, and this panel will discuss some of the policy approaches for moving towards universally usable e-government services.", + "citation_count": 0, + "topics": [ + "E-Government and Public Services", + "Privacy, Security, and Data Protection" + ], + "discovered_via": [ + "neighbor:openalex:W4392182430" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2359053655", + "title": "The Philosophical Wisdom of Wang Guowei's \"Self-realm\" and \"Selfless Realm\"", + "year": 2010, + "url": "http://en.cnki.com.cn/Article_en/CJFDTOTAL-HYSZ201005025.htm", + "doi": null, + "abstract": "During the creation of self-realm and selfless realm,Wang Guowei digested the philosophy of describing in terms of objects and meso.He transcended the and individual and situation and things of traditional aesthetics through a kind of philosophical wisdom.He thought that the philosophical emmanuel would be used to divide the self-realm and selfless realm.Therefore,self-realm and selfless realm must belong to an aesthetic category filled with philosophical wisdom.", + "citation_count": 0, + "topics": [ + "Educational Reforms and Innovations", + "Digital Media and Visual Art" + ], + "discovered_via": [ + "neighbor:openalex:W4383913712" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2387777532", + "title": "\"Creating Realm\" and \"Writing Realm\":On the Formation of \"Realm Theory\" Proposed by Wang Guowei", + "year": 2010, + "url": "http://en.cnki.com.cn/Article_en/CJFDTOTAL-MYSF201012011.htm", + "doi": null, + "abstract": "Theory is proposed by Wang Guowei in his literary critic book Ren Jian Ci Hua,which is regarded as inheritance,fusion and development to Chinese and Western aesthetic ideology.According to the Theory,the formation of is composed of such specific steps as observing,getting access to,creating realm and writing realm,etc.The last steps of creating realm and writing realm,however,are mingled with each other.Therefore,this paper presents the concepts of observing,getting access to,creating realm and writing realm,and conducts an analysis of the fusion of creating Realm and writing Realm from an aesthetic prospective.", + "citation_count": 0, + "topics": [ + "Language, Metaphor, and Cognition", + "Discourse Analysis in Language Studies" + ], + "discovered_via": [ + "neighbor:openalex:W4383913712" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2352855287", + "title": "Exploration into the Relation between the College Originality Culture and the Construction of Originality Courses System under the Sketch of the General Moral Education", + "year": 2010, + "url": "http://en.cnki.com.cn/Article_en/CJFDTOTAL-LCHK201001004.htm", + "doi": null, + "abstract": "With premises that originality college culture is of the most importance to the college culture and provides core impetus for colleges to make increasing improvements,and that originality courses system under the sketch of the general moral education is the platform to practice the originality culture,this essay explores the necessity of the combination between the research on the originality college culture and the practice of originality courses system,the importance of theories guiding the building of the originality courses system,and the principles of project-devising of originality courses under the sketch of general moral education.", + "citation_count": 0, + "topics": [ + "Ideological and Political Education" + ], + "discovered_via": [ + "neighbor:openalex:W4381684347" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W2338818750", + "title": "INTRODUCING AN OPERATIVE AND SUPERVISION GUIDELINE FOR HIP FRACTURE SURGERY REDUCED THE RATE OF RE-OPERATION FROM 15% TO 10%", + "year": 2010, + "url": "https://online.boneandjoint.org.uk/doi/abs/10.1302/0301-620X.92BSUPP_II.0920307a", + "doi": null, + "abstract": "Introduction: We derived an exhaustive operative and supervision guideline for the treatment of hip fractures from the current international and own published literature, and implemented the guidelines in our department. Methods: 1274 unselected consecutive patients admitted with a hip fracture were included, 336 of these prospectively after implementation of the new guideline. Demographic parameters, hospital treatment and re-operations were assessed from patient journals. Re-operations were recorded after six months. Results: 95% (320/336) of operative procedures were found to have followed the new guideline treatment compared to 78% (733/938) prior to its introduction (p After implementing the guideline, the rate of unsupervised junior registrars performing operations declined from 20% (188/938) to 6% (21/336, p Conclusion: An exhaustive operative guideline for hip fracture treatment can be implemented. In our case, the guideline both raised the rate of supervision and reduced the rate of reoperations.", + "citation_count": 0, + "topics": [ + "Clinical practice guidelines implementation", + "Hip and Femur Fractures", + "Cardiac, Anesthesia and Surgical Outcomes" + ], + "discovered_via": [ + "neighbor:openalex:W4407173730" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; topic-token-hits=0; exclude=none", + "heuristic_relevance_score": 0.0, + "heuristic_relevance_reason": "include=none; topic-token-hits=0; exclude=none" + }, + { + "id": "openalex:W3124297489", + "title": "Emerging Topics In Corporate Governance", + "year": 2010, + "url": "https://ideas.repec.org/h/wsi/wschap/9789814289351_0001.html", + "doi": null, + "abstract": "AbstractThe following sections are included:Part 1 Corporate Governance and the Financial MarketsPart 2 Corporate Governance and the Regulatory EnvironmentPart 3 Corporate Governance and Stakeholder EngagementPart 4 Corporate Governance in Different Contexts", + "citation_count": 0, + "topics": [ + "Global Financial Regulation and Crises", + "Securities Regulation and Market Practices", + "Corporate Governance and Law" + ], + "discovered_via": [ + "neighbor:openalex:W4400303336" + ], + "relevance_score": 0.0, + "relevance_reason": "include=none; 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b/examples/ai-in-hiring-processes/data/claw_tasks.json new file mode 100644 index 0000000..3ca2b73 --- /dev/null +++ b/examples/ai-in-hiring-processes/data/claw_tasks.json @@ -0,0 +1,111 @@ +{ + "mode": "claw", + "topic": "AI in Hiring Processes", + "coordinator_instruction": "Read AGENTS.md and topic.json. Assign only accepted research-contract tasks. Do not publish when no new paper is accepted.", + "tasks": [ + { + "id": "queries", + "role": "query_designer", + "objective": "Review topic.json and improve search_queries without broadening excluded scope.", + "inputs": [ + "topic.json" + ], + "outputs": [ + "topic.json" + ], + "depends_on": [] + }, + { + "id": "discover", + "role": "graph_scout", + "objective": "Run scholarly graph discovery and produce candidates with provenance.", + "inputs": [ + "topic.json", + "data/papers.json" + ], + "outputs": [ + "data/candidates.json" + ], + "depends_on": [ + "queries" + ] + }, + { + "id": "review", + "role": "relevance_reviewer", + "objective": "Apply inclusion/exclusion rules and propose accepted candidate IDs.", + "inputs": [ + "topic.json", + "data/candidates.json" + ], + "outputs": [ + "review decision" + ], + "depends_on": [ + "discover" + ] + }, + { + "id": "library", + "role": "librarian", + "objective": "Accept approved IDs, deduplicate, and preserve source metadata.", + "inputs": [ + "data/candidates.json", + "review decision" + ], + "outputs": [ + "data/papers.json" + ], + "depends_on": [ + "review" + ] + }, + { + "id": "synthesis", + "role": "analyst", + "objective": "Classify papers and rebuild Markdown notes and synthesis.", + "inputs": [ + "topic.json", + "data/papers.json" + ], + "outputs": [ + "reports/research_report.md", + "reports/papers/" + ], + "depends_on": [ + "library" + ] + }, + { + "id": "gaps", + "role": "gap_analyst", + "objective": "Use skills/analyze-research-gaps/SKILL.md to write evidence-backed opportunities.", + "inputs": [ + "data/papers.json", + "reports/research_report.md" + ], + "outputs": [ + "data/research_opportunities.json" + ], + "depends_on": [ + "synthesis" + ] + }, + { + "id": "publish", + "role": "publisher", + "objective": "Regenerate dashboard, graph, wiki, trends, and opportunity view.", + "inputs": [ + "data/papers.json", + "data/research_opportunities.json" + ], + "outputs": [ + "topic-dashboard.html", + "data/dashboard.json" + ], + "depends_on": [ + "gaps" + ] + } + ] +} diff --git a/examples/ai-in-hiring-processes/data/dashboard.json b/examples/ai-in-hiring-processes/data/dashboard.json new file mode 100644 index 0000000..8504acc --- /dev/null +++ b/examples/ai-in-hiring-processes/data/dashboard.json @@ -0,0 +1,2274 @@ +{ + "topic": { + "slug": "ai-in-hiring-processes", + "topic": "AI in Hiring Processes", + "raw_intent": "How AI could impact on the hiring process?", + "research_question": "How does AI affect the design, execution, outcomes, and governance of hiring processes across sourcing, screening, assessment, interviewing, selection, and candidate communication?", + "goal": "Identify where AI can create measurable hiring value, where it introduces operational or legal risk, and which evidence supports responsible adoption and evaluation.", + "audience": "Talent acquisition leaders, HR operations teams, hiring managers, AI system designers, legal and compliance teams, and organizational decision-makers.", + "include": [ + "AI applications across the end-to-end hiring lifecycle", + "Decision-support, automation, and human-in-the-loop hiring systems", + "Impacts on hiring speed, cost, quality, consistency, and candidate experience", + "Validity, reliability, fairness, bias, accessibility, privacy, transparency, and explainability", + "Performance differences across roles, industries, applicant groups, and labor-market contexts", + "Data, workflow, monitoring, escalation, audit, and accountability requirements", + "Applicable employment, anti-discrimination, data-protection, and automated-decision constraints", + "Failure modes including proxy discrimination, feedback loops, gaming, automation bias, and model drift" + ], + "exclude": [ + "AI impacts on general workforce management after hiring", + "Employee productivity, retention, or performance-management systems unless directly used to validate hiring outcomes", + "Unsupported vendor marketing claims", + "Fully autonomous hiring recommendations without evaluation of human oversight and governance", + "Claims about legal compliance that are not tied to a defined jurisdiction" + ], + "years": { + "from": 2023, + "to": 2026 + }, + "evidence_types": [ + "methods", + "benchmarks", + "systems", + "surveys" + ], + "taxonomy": [ + "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication", + "AI function: generation, prediction, ranking, classification, recommendation, conversation, workflow automation", + "Decision role: assistive, advisory, semi-automated, autonomous", + "Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security", + "Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor", + "Risk mechanism: biased data, proxy variables, measurement error, distribution shift, gaming, hallucination, automation bias, weak oversight", + "Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance" + ], + "dashboard_sections": [ + "AI use cases by hiring stage and decision role", + "Business-value metrics: time-to-hire, cost-per-hire, recruiter workload, conversion, and quality-of-hire", + "Evidence quality and benchmark comparability", + "Predictive validity, reliability, and generalization across jobs and populations", + "Fairness outcomes and adverse-impact analysis by applicant group", + "Candidate experience, accessibility, transparency, and contestability", + "Human-AI workflow design, override behavior, and accountability", + "Legal and policy requirements by jurisdiction", + "Privacy, security, data provenance, and vendor-risk controls", + "Adversarial risks, failure modes, monitoring signals, and mitigations" + ], + "cadence": "weekly", + "approval_required": false, + "search_queries": [ + "artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection", + "AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability", + "AI recruitment system human in the loop recruiter decision support workflow architecture monitoring", + "algorithmic hiring employment discrimination automated decision systems law regulation audit privacy", + "AI recruiting business value time to hire cost per hire quality of hire recruiter productivity ROI", + "algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability", + "AI hiring adversarial gaming prompt injection resume manipulation deepfake interview fraud automation bias model drift", + "generative AI recruiting systems candidate communication job descriptions interview assistants", + "algorithmic hiring survey employers recruiters applicants adoption trust attitudes", + "AI hiring methods causal evaluation field experiment longitudinal quality of hire" + ], + "scouting_strategy": [ + "Map evidence to each hiring stage before comparing impacts across the full process.", + "Separate technical benchmark performance from real-world organizational outcomes and candidate impacts.", + "Prioritize studies with defined tasks, populations, baselines, subgroup analyses, and reproducible evaluation methods.", + "Compare AI-only, human-only, and human-AI workflows where evidence permits.", + "Extract business-value measures separately from validity, fairness, privacy, and candidate-experience measures.", + "Record jurisdiction, job type, labor-market setting, deployment scale, and vendor dependence for each system.", + "Assess agent-system requirements including tool permissions, data boundaries, human approval points, audit logs, escalation paths, and continuous monitoring.", + "Search for both intended benefits and adversarial or failure-oriented evidence, including applicant gaming and recruiter automation bias." + ], + "intent_refinement_provider": "codex", + "intent_refinement_model": "codex-cli:configured-model", + "created_at": "2026-06-19" + }, + "paper_count": 8, + "visualized_paper_count": 8, + "visualized_source": "accepted papers", + "categories": [ + { + "id": "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication", + "label": "Hiring Stage: Sourcing, Matching, Screening, Assessment, Interviewing, Selection, Communication", + "count": 4, + "ratio": 0.5, + "color": "#e4572e" + }, + { + "id": "AI function: generation, prediction, ranking, classification, recommendation, conversation, workflow automation", + "label": "Ai Function: Generation, Prediction, Ranking, Classification, Recommendation, Conversation, Workflow Automation", + "count": 0, + "ratio": 0.0, + "color": "#1d6f75" + }, + { + "id": "Decision role: assistive, advisory, semi-automated, autonomous", + "label": "Decision Role: Assistive, Advisory, Semi-Automated, Autonomous", + "count": 0, + "ratio": 0.0, + "color": "#f3a712" + }, + { + "id": "Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security", + "label": "Evaluation Dimension: Utility, Validity, Reliability, Efficiency, Fairness, Privacy, Transparency, Accessibility, Security", + "count": 1, + "ratio": 0.125, + "color": "#963484" + }, + { + "id": "Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor", + "label": "Stakeholder: Applicant, Recruiter, Hiring Manager, Employer, Vendor, Regulator, Auditor", + "count": 2, + "ratio": 0.25, + "color": "#4267ac" + }, + { + "id": "Risk mechanism: biased data, proxy variables, measurement error, distribution shift, gaming, hallucination, automation bias, weak oversight", + "label": "Risk Mechanism: Biased Data, Proxy Variables, Measurement Error, Distribution Shift, Gaming, Hallucination, Automation Bias, Weak Oversight", + "count": 0, + "ratio": 0.0, + "color": "#5b8e3e" + }, + { + "id": "Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance", + "label": "Governance Control: Documentation, Consent, Human Review, Appeal, Audit, Monitoring, Incident Response, Vendor Assurance", + "count": 1, + "ratio": 0.125, + "color": "#7b6045" + } + ], + "runs": [ + { + "date": "2026-06-19", + "queries": [ + "artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection", + "AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability", + "AI recruitment system human in the loop recruiter decision support workflow architecture monitoring", + "algorithmic hiring employment discrimination automated decision systems law regulation audit privacy", + "AI recruiting business value time to hire cost per hire quality of hire recruiter productivity ROI", + "algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability", + "AI hiring adversarial gaming prompt injection resume manipulation deepfake interview fraud automation bias model drift", + "generative AI recruiting systems candidate communication job descriptions interview assistants", + "algorithmic hiring survey employers recruiters applicants adoption trust attitudes", + "AI hiring methods causal evaluation field experiment longitudinal quality of hire" + ], + "accepted_ids": [], + "accepted_count": 0, + "candidate_count": 540, + "cost": { + "provider": "openalex", + "token_count": 0, + "money_cost_usd": 0.0, + "currency": "USD", + "note": "OpenAlex discovery does not invoke a model; token usage is zero." + }, + "accepted": 0, + "cumulative": 0, + "accepted_topic_counts": { + "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication": 0, + "AI function: generation, prediction, ranking, classification, recommendation, conversation, workflow automation": 0, + "Decision role: assistive, advisory, semi-automated, autonomous": 0, + "Evaluation 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deepfake interview fraud automation bias model drift", + "generative AI recruiting systems candidate communication job descriptions interview assistants", + "algorithmic hiring survey employers recruiters applicants adoption trust attitudes", + "AI hiring methods causal evaluation field experiment longitudinal quality of hire" + ], + "accepted_ids": [], + "accepted_count": 0, + "candidate_count": 540, + "cost": { + "provider": "codex", + "model": "codex-cli:configured-model", + "token_count": 40597, + "input_tokens": 38704, + "output_tokens": 1849, + "reasoning_tokens": 44, + "money_cost_usd": 0.0, + "currency": "USD", + "note": "Codex CLI reports token usage but does not expose billable USD cost in this workflow." + }, + "accepted": 0, + "cumulative": 0, + "accepted_topic_counts": { + "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication": 0, + "AI function: generation, prediction, ranking, classification, recommendation, conversation, workflow automation": 0, + "Decision role: assistive, advisory, semi-automated, autonomous": 0, + "Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security": 0, + "Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor": 0, + "Risk mechanism: biased data, proxy variables, measurement error, distribution shift, gaming, hallucination, automation bias, weak oversight": 0, + "Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance": 0 + }, + "cumulative_topics": { + "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication": 0, + "AI function: generation, prediction, ranking, classification, recommendation, conversation, workflow automation": 0, + "Decision role: assistive, advisory, semi-automated, autonomous": 0, + "Evaluation dimension: utility, validity, reliability, efficiency, 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"AI hiring adversarial gaming prompt injection resume manipulation deepfake interview fraud automation bias model drift", + "generative AI recruiting systems candidate communication job descriptions interview assistants", + "algorithmic hiring survey employers recruiters applicants adoption trust attitudes", + "AI hiring methods causal evaluation field experiment longitudinal quality of hire" + ], + "accepted_ids": [ + "openalex:W4409825334", + "openalex:W4404228668", + "openalex:W4394590365", + "openalex:W4386714740", + "openalex:W4391598857", + "openalex:W4366989571", + "openalex:W4390880618" + ], + "accepted_count": 7, + "candidate_count": 540, + "cost": { + "provider": "codex", + "model": "codex-cli:configured-model", + "token_count": 40624, + "input_tokens": 38704, + "output_tokens": 1876, + "reasoning_tokens": 44, + "money_cost_usd": 0.0, + "currency": "USD", + "note": "Codex CLI reports token usage but does not expose billable USD cost in this workflow." + }, + "accepted": 7, + "cumulative": 8, + "accepted_topic_counts": { + "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication": 3, + "AI function: generation, prediction, ranking, classification, recommendation, conversation, workflow automation": 0, + "Decision role: assistive, advisory, semi-automated, autonomous": 0, + "Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security": 1, + "Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor": 2, + "Risk mechanism: biased data, proxy variables, measurement error, distribution shift, gaming, hallucination, automation bias, weak oversight": 0, + "Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance": 1 + }, + "cumulative_topics": { + "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication": 4, + "AI function: generation, prediction, ranking, classification, recommendation, conversation, workflow automation": 0, + "Decision role: assistive, advisory, semi-automated, autonomous": 0, + "Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security": 1, + "Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor": 2, + "Risk mechanism: biased data, proxy variables, measurement error, distribution shift, gaming, hallucination, automation bias, weak oversight": 0, + "Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance": 1 + }, + "cumulative_topic_ratios": { + "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication": 0.5, + "AI function: generation, prediction, ranking, classification, recommendation, conversation, workflow automation": 0.0, + "Decision role: assistive, advisory, semi-automated, autonomous": 0.0, + "Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security": 0.125, + "Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor": 0.25, + "Risk mechanism: biased data, proxy variables, measurement error, distribution shift, gaming, hallucination, automation bias, weak oversight": 0.0, + "Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance": 0.125 + }, + "token_count": 40624, + "money_cost_usd": 0.0, + "currency": "USD" + } + ], + "scout_tokens": 81221, + "scout_spend_usd": 0.0, + "candidate_count": 540, + "candidate_generated_at": "2026-06-20T10:44:04.835225+00:00", + "candidate_cost": { + "provider": "codex", + "model": "codex-cli:configured-model", + "token_count": 40571, + "input_tokens": 38704, + "output_tokens": 1789, + "reasoning_tokens": 78, + "money_cost_usd": 0.0, + "currency": "USD", + "note": "Codex CLI reports token usage but does not expose billable USD cost in this workflow." + }, + "candidates": [ + { + "id": "openalex:W4409825334", + "title": "Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity", + "year": 2025, + "url": "https://doi.org/10.3389/fpsyg.2025.1564979", + "doi": "https://doi.org/10.3389/fpsyg.2025.1564979", + "abstract": "This paper explores the efficacy of AI-driven chatbots in accurately inferring personality traits compared to traditional psychometric tests within a real-world professional hiring context. The study is driven by the increasing integration of AI tools in recruitment processes, which necessitates a deeper understanding of their reliability and validity. Using a quasi-experimental design with propensity score matching, we analysed data from 159 candidates and other professionals from Serbian and Montenegrin regions who completed both traditional psychometric assessments and AI-based personality evaluations based on the Big Five Personality model. A novel one-question-per-facet approach was employed in the chatbot assessments with a goal of enabling more granular analysis of the chatbot's psychometric properties. The findings indicate that the chatbot demonstrated good structural, substantive, and convergent validity for certain traits, particularly Extraversion and Conscientiousness, but not for Neuroticism, Agreeableness, and Openness. While robust regression confirmed that AI-inferred scores are less susceptible to social desirability bias than traditional tests, they did not significantly predict real-world outcomes, indicating issues with external validity, particularly predictive validity. The results suggest that AI-driven chatbots show promise for identifying certain personality traits and demonstrate resistance to social desirability bias. This paper contributes to the emerging field of AI and psychometrics by offering insights into the potential and limitations of AI tools in professional selection, while developing an approach for refining psychometric properties of AI-driven assessments.", + "citation_count": 2, + "topics": [ + "Personality Traits and Psychology", + "Employer Branding and e-HRM", + "AI in Service Interactions" + ], + "discovered_via": [ + "query:AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability" + ], + "relevance_score": 10.0, + "relevance_reason": "Directly evaluates an AI hiring assessment against psychometric tests using reliability, convergent validity, predictive validity, and social-desirability outcomes.", + "heuristic_relevance_score": 1.2, + "heuristic_relevance_reason": "include=none; topic-token-hits=2; exclude=none" + }, + { + "id": "openalex:W4404228668", + "title": "Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE)", + "year": 2024, + "url": "https://doi.org/10.56294/dm2025487", + "doi": "https://doi.org/10.56294/dm2025487", + "abstract": "IntroductionThis research aims to explore the effectiveness and inclusivity of AI-powered recruitment tools in hiring people with disabilities within the United Arab Emirates. Such is the situation where AI integration into the arena of recruitment is increasingly rapid, while there are vital issues on the side of bias, accessibility, and fairness for applicants of diverse needs. MethodsThis study was a mixed-methods approach, examining sentiment analysis, emotion detection, and HR analytics of feedback from applicants with a disability, 415 in total. The research focused on scores referring to sentiment, the progression rate, and the outcome of the final hiring. ResultsThe sentiment score varied significantly across disability types (p-value <0.05). The applicants with cognitive disability expressed the highest sentiment sore while applicants with hearing impairment had the lowest, which indicated the varying adaptability of AI. The emotion analysis depicted a mix of positive and negative emotions. A few applicants liked technology and have trust in it, while others report fear. Clearly, the applicants, both disabled and non-disabled did not differ in their rate of progression (p-value >0.05), hence never indicating any significant difference within the initial steps of the process. The final hiring stage showed significant differences in results with (p-value <0.05), where the proportionate number of disabled applicants was recorded to be lower than that of non-disabled applicants", + "citation_count": 110, + "topics": [ + "Organizational and Employee Performance", + "AI in Service Interactions", + "Cyberloafing and Workplace Behavior" + ], + "discovered_via": [ + "query:algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability" + ], + "relevance_score": 9.0, + "relevance_reason": "Direct mixed-methods evaluation of AI recruitment accessibility, fairness, candidate experience, progression, and hiring outcomes for applicants with disabilities in the UAE.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4394590365", + "title": "Fairness, AI & recruitment", + "year": 2024, + "url": "https://doi.org/10.1016/j.clsr.2024.105966", + "doi": "https://doi.org/10.1016/j.clsr.2024.105966", + "abstract": "The ever-increasing adoption of AI technologies in the hiring landscape to enhance human resources efficiency raises questions about algorithmic decision-making's implications in employment, especially for job applicants, including those at higher risk of social discrimination. Among other concepts, such as transparency and accountability, fairness has become crucial in AI recruitment debates due to the potential reproduction of bias and discrimination that can disproportionately affect certain vulnerable groups. However, the ideals and ambitions of fairness may signify different meanings to various stakeholders. Conceptualizing fairness is critical because it may provide a clear benchmark for evaluating and mitigating biases, ensuring that AI systems do not perpetuate existing imbalances and promote, in this case, equitable opportunities for all candidates in the job market. To this end, in this article, we conduct a scoping literature review on fairness in AI applications for recruitment and selection purposes, with special emphasis on its definition, categorization, and practical implementation. We start by explaining how AI applications have been increasingly used in the hiring process, especially to increase the efficiency of the HR team. We then move to the limitations of this technological innovation, which is known to be at high risk of privacy violations and social discrimination. Against this backdrop, we focus on defining and operationalizing fairness in AI applications for recruitment and selection purposes through cross-disciplinary lenses. Although the applicable legal frameworks and some research currently address the issue piecemeal, we observe and welcome the emergence of some cross-disciplinary efforts aimed at tackling this multifaceted challenge. We conclude the article with some brief recommendations to guide and shape future research and action on the fairness of AI applications in the hiring process for the better.", + "citation_count": 77, + "topics": [ + "Employer Branding and e-HRM", + "Ethics and Social Impacts of AI", + "Names, Identity, and Discrimination Research" + ], + "discovered_via": [ + "query:algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability" + ], + "relevance_score": 9.0, + "relevance_reason": "Hiring-specific scoping review addressing fairness definitions, operationalization, bias, discrimination, privacy, transparency, accountability, and legal frameworks.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4386714740", + "title": "Ethics and discrimination in artificial intelligence-enabled recruitment practices", + "year": 2023, + "url": "https://doi.org/10.1057/s41599-023-02079-x", + "doi": "https://doi.org/10.1057/s41599-023-02079-x", + "abstract": "Abstract This study aims to address the research gap on algorithmic discrimination caused by AI-enabled recruitment and explore technical and managerial solutions. The primary research approach used is a literature review. The findings suggest that AI-enabled recruitment has the potential to enhance recruitment quality, increase efficiency, and reduce transactional work. However, algorithmic bias results in discriminatory hiring practices based on gender, race, color, and personality traits. The study indicates that algorithmic bias stems from limited raw data sets and biased algorithm designers. To mitigate this issue, it is recommended to implement technical measures, such as unbiased dataset frameworks and improved algorithmic transparency, as well as management measures like internal corporate ethical governance and external oversight. Employing Grounded Theory, the study conducted survey analysis to collect firsthand data on respondents\u2019 experiences and perceptions of AI-driven recruitment applications and discrimination.", + "citation_count": 351, + "topics": [ + "Ethics and Social Impacts of AI", + "Employer Branding and e-HRM", + "AI and HR Technologies" + ], + "discovered_via": [ + "query:artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection" + ], + "relevance_score": 8.0, + "relevance_reason": "Directly addresses AI recruitment efficiency, discriminatory outcomes, biased data and designers, transparency, governance, and external oversight, though its methods description is unclear.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4391598857", + "title": "A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring", + "year": 2024, + "url": "https://doi.org/10.3390/ai5010019", + "doi": "https://doi.org/10.3390/ai5010019", + "abstract": "The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms\u2019 fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society.", + "citation_count": 112, + "topics": [ + "AI and HR Technologies", + "Artificial Intelligence in Healthcare and Education", + "AI in Service Interactions" + ], + "discovered_via": [ + "query:algorithmic hiring employment discrimination automated decision systems law regulation audit privacy" + ], + "relevance_score": 8.0, + "relevance_reason": "Direct review of bias mitigation in AI CV screening, covering efficiency, fairness techniques, case studies, and human-machine collaboration.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4366989571", + "title": "Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring", + "year": 2023, + "url": "https://doi.org/10.1111/1748-8583.12511", + "doi": "https://doi.org/10.1111/1748-8583.12511", + "abstract": "Abstract Despite frequent claims that increased use of artificial intelligence (AI) in hiring will reduce the human bias that has long plagued recruitment and selection, AI may equally replicate and amplify such bias and embed it in technology. This article explores exclusion and inclusion in AI\u2010supported hiring, focusing on three interrelated areas: data, design and decisions. It is suggested that in terms of data, organisational fit, categorisations and intersectionality require consideration in relation to exclusion. As various stakeholders collaborate to create AI, it is essential to explore which groups are dominant and how subjective assessments are encoded in technology. Although AI\u2010supported hiring should enhance recruitment decisions, evidence is lacking on how humans and machines interact in decision\u2010making, and how algorithms can be audited and regulated effectively for inclusion. This article recommends areas for interrogation through further research, and contributes to understanding how algorithmic inclusion can be achieved in AI\u2010supported hiring.", + "citation_count": 79, + "topics": [ + "Ethics and Social Impacts of AI", + "Employer Branding and e-HRM", + "Digital Economy and Work Transformation" + ], + "discovered_via": [ + "query:algorithmic hiring employment discrimination automated decision systems law regulation audit privacy" + ], + "relevance_score": 8.0, + "relevance_reason": "Directly analyzes inclusion risks in AI-supported hiring across data, design, human-machine decisions, auditing, and regulation, though it appears primarily conceptual.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4390880618", + "title": "A UTAUT-Based Framework for Analyzing Users\u2019 Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand", + "year": 2024, + "url": "https://doi.org/10.3390/systems12010028", + "doi": "https://doi.org/10.3390/systems12010028", + "abstract": "Recruitment is a fundamental aspect of Human Resource Management to drive organizational performance. Traditional recruitment processes, with manual stages, are time-consuming and inefficient. Artificial Intelligence (AI), which demonstrates its potential in various sectors such as healthcare, education, and notable cases of ChatGPT, is currently reshaping recruitment by automating tasks to improve efficiency. However, in Thailand, where there is a growing demand for talents, the application of AI in recruitment remains relatively limited. This study focuses on human resources (HR) and recruitment professionals in Thailand, aiming to understand their perspectives on the integration of AI in recruitment. It utilized the Unified Theory for Acceptance and Use of Technology (UTAUT) model, customized to suit the specific requirements of Thailand recruitment practices. The study explores the factors influencing users\u2019 intention to adopt AI in recruitment. Survey questionnaire items were created based on prior literature and refined with insights from HR and recruitment experts to ensure applicability in the context of recruitment in Thailand. A survey involving 364 HR and recruiting professionals in the Bangkok metropolitan area supplied comprehensive responses. The study reveals that several factors, including perceived value, perceived autonomy, effort expectancy, and facilitating conditions, significantly impact the intention to adopt AI for recruitment. While social influence and trust in AI technology do not have a direct influence on intention, social influence directly affects perceived value. Trust in AI technology positively influences Effort Expectancy. This study provides valuable benefits for HR and recruitment professionals, organizations, and AI developers by offering insights into AI adoption and sustainability, enhancing recruitment processes and promoting the effective use of AI tools in this sector.", + "citation_count": 80, + "topics": [ + "Employer Branding and e-HRM", + "AI and HR Technologies", + "Technology Adoption and User Behaviour" + ], + "discovered_via": [ + "query:algorithmic hiring survey employers recruiters applicants adoption trust attitudes" + ], + "relevance_score": 7.0, + "relevance_reason": "Hiring-specific survey of 364 recruitment professionals measuring AI adoption factors including value, autonomy, effort, conditions, and trust in Thailand.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4315628734", + "title": "Applicants\u2019 Fairness Perceptions of Algorithm-Driven Hiring Procedures", + "year": 2023, + "url": "https://doi.org/10.1007/s10551-022-05320-w", + "doi": "https://doi.org/10.1007/s10551-022-05320-w", + "abstract": "", + "citation_count": 80, + "topics": [ + "Ethics and Social Impacts of AI", + "Employer Branding and e-HRM", + "Privacy, Security, and Data Protection" + ], + "discovered_via": [ + "query:algorithmic hiring survey employers recruiters applicants adoption trust attitudes" + ], + "relevance_score": 7.0, + "relevance_reason": "The title directly targets applicant fairness perceptions of algorithm-driven hiring, but the missing abstract prevents assessment of methods and findings.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4288083801", + "title": "Mitigating bias in algorithmic hiring", + "year": 2020, + "url": "https://doi.org/10.1145/3351095.3372828", + "doi": "https://doi.org/10.1145/3351095.3372828", + "abstract": "There has been rapidly growing interest in the use of algorithms in hiring, especially as a means to address or mitigate bias. Yet, to date, little is known about how these methods are used in practice. How are algorithmic assessments built, validated, and examined for bias? In this work, we document and analyze the claims and practices of companies offering algorithms for employment assessment. In particular, we identify vendors of algorithmic pre-employment assessments (i.e., algorithms to screen candidates), document what they have disclosed about their development and validation procedures, and evaluate their practices, focusing particularly on efforts to detect and mitigate bias. Our analysis considers both technical and legal perspectives. Technically, we consider the various choices vendors make regarding data collection and prediction targets, and explore the risks and trade-offs that these choices pose. We also discuss how algorithmic de-biasing techniques interface with, and create challenges for, antidiscrimination law.", + "citation_count": 626, + "topics": [ + "Ethics and Social Impacts of AI", + "Digital Economy and Work Transformation", + "Names, Identity, and Discrimination Research" + ], + "discovered_via": [ + "neighbor:openalex:W4392871535" + ], + "relevance_score": 6.0, + "relevance_reason": "Highly relevant analysis of validation, bias, vendor practices, and antidiscrimination law in algorithmic pre-employment screening, but published before 2023.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4382520149", + "title": "The Impact of AI on Recruitment and Selection Processes: Analysing the role of AI in automating and enhancing recruitment and selection procedures", + "year": 2023, + "url": "https://doi.org/10.55938/ijgasr.v2i2.50", + "doi": "https://doi.org/10.55938/ijgasr.v2i2.50", + "abstract": "Human resource management is the process of identifying, recruiting, hiring, and training talented individuals, as well as providing them with career advancement possibilities and critical feedback on their performance. The purpose of this study was to investigate the function of AI in HRM practises using qualitative bibliometric analysis. Scopus, emerald, and the Jstore library are used as data sources. This analysis contains adjustments to data spanning 18 years. It also showed that there is a constant improvement and introduction of new technological conveniences. In accordance with the present market climate, which promotes and celebrates process management and people management practises targeted at making the organisation economically viable and different from the competition, this is a positive development. This work advances the theoretical understanding of AI's growth in the HR sector in light of this reality. Articles and proceedings examined in this research reveal that different authors and academic institutions provide different perspectives on the problem.", + "citation_count": 104, + "topics": [ + "Computability, Logic, AI Algorithms", + "Big Data and Business Intelligence", + "Artificial Intelligence in Healthcare and Education" + ], + "discovered_via": [ + "query:artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection" + ], + "relevance_score": 5.0, + "relevance_reason": "Directly concerns AI in recruitment and selection, but the abstract is a broad bibliometric HRM discussion with little concrete hiring evidence or evaluation.", + "heuristic_relevance_score": 1.2, + "heuristic_relevance_reason": "include=none; topic-token-hits=2; exclude=none" + }, + { + "id": "openalex:W4383913712", + "title": "Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT", + "year": 2023, + "url": "https://doi.org/10.1111/1748-8583.12524", + "doi": "https://doi.org/10.1111/1748-8583.12524", + "abstract": "Abstract ChatGPT and its variants that use generative artificial intelligence (AI) models have rapidly become a focal point in academic and media discussions about their potential benefits and drawbacks across various sectors of the economy, democracy, society, and environment. It remains unclear whether these technologies result in job displacement or creation, or if they merely shift human labour by generating new, potentially trivial or practically irrelevant, information and decisions. According to the CEO of ChatGPT, the potential impact of this new family of AI technology could be as big as \u201cthe printing press\u201d, with significant implications for employment, stakeholder relationships, business models, and academic research, and its full consequences are largely undiscovered and uncertain. The introduction of more advanced and potent generative AI tools in the AI market, following the launch of ChatGPT, has ramped up the \u201cAI arms race\u201d, creating continuing uncertainty for workers, expanding their business applications, while heightening risks related to well\u2010being, bias, misinformation, context insensitivity, privacy issues, ethical dilemmas, and security. Given these developments, this perspectives editorial offers a collection of perspectives and research pathways to extend HRM scholarship in the realm of generative AI. In doing so, the discussion synthesizes the literature on AI and generative AI, connecting it to various aspects of HRM processes, practices, relationships, and outcomes, thereby contributing to shaping the future of HRM research.", + "citation_count": 760, + "topics": [ + "Artificial Intelligence in Healthcare and Education", + "AI and HR Technologies", + "Ethics and Social Impacts of AI" + ], + "discovered_via": [ + "query:artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection" + ], + "relevance_score": 4.0, + "relevance_reason": "Discusses generative AI across HRM and relevant risks such as bias and privacy, but the abstract does not establish a substantive hiring-specific analysis.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2072937224", + "title": "Efficient multifaceted screening of job applicants", + "year": 2013, + "url": "https://doi.org/10.1145/2452376.2452453", + "doi": "https://doi.org/10.1145/2452376.2452453", + "abstract": "Built on top of human resources management databases within the enterprise, we present a decision support system for managing and optimizing screening activities during the hiring process in a large organization. The basic idea is to prioritize the efforts of human resource practitioners to focus on candidates that are likely of high quality, that are likely to accept a job offer if made one, and that are likely to remain with the organization for the long term. To do so, the system first individually ranks candidates along several dimensions using a keyword matching algorithm and several bipartite ranking algorithms with univariate loss trained on historical actions. Next, individual rankings are aggregated to derive a single list that is presented to the recruitment team through an interactive portal. The portal supports multiple filters that facilitate effective identification of candidates. We demonstrate the usefulness of our system on data collected from a large organization over several years with business value metrics showing greater hiring yield with less interviews. Similarly, using historical pre-hire data we demonstrate accurate identification of candidates that will have quickly left the organization. The system has been deployed as described in a large globally integrated enterprise.", + "citation_count": 30, + "topics": [ + "Employer Branding and e-HRM", + "Data Mining Algorithms and Applications", + "Scheduling and Timetabling Solutions" + ], + "discovered_via": [ + "neighbor:openalex:W4381684347" + ], + "relevance_score": 4.0, + "relevance_reason": "Directly presents algorithmic candidate ranking and decision support with hiring-yield evidence, but predates the window and partly optimizes post-hire retention.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4381848566", + "title": "Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation", + "year": 2023, + "url": "https://doi.org/10.1016/j.inffus.2023.101896", + "doi": "https://doi.org/10.1016/j.inffus.2023.101896", + "abstract": "Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system\u2019s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system\u2019s life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight; robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental wellbeing; and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society.", + "citation_count": 654, + "topics": [ + "Ethics and Social Impacts of AI", + "Adversarial Robustness in Machine Learning", + "Artificial Intelligence in Healthcare and Education" + ], + "discovered_via": [ + "query:artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection" + ], + "relevance_score": 2.0, + "relevance_reason": "Provides general AI oversight, fairness, privacy, transparency, accountability, and auditing concepts, but no hiring-specific application.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4400303336", + "title": "Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making", + "year": 2024, + "url": "https://doi.org/10.3389/fhumd.2024.1421273", + "doi": "https://doi.org/10.3389/fhumd.2024.1421273", + "abstract": "The rapid integration of artificial intelligence (AI) systems into various domains has raised concerns about their impact on individual and societal wellbeing, particularly due to the lack of transparency and accountability in their decision-making processes. This review aims to provide an overview of the key legal and ethical challenges associated with implementing transparency and accountability in AI systems. The review identifies four main thematic areas: technical approaches, legal and regulatory frameworks, ethical and societal considerations, and interdisciplinary and multi-stakeholder approaches. By synthesizing the current state of research and proposing key strategies for policymakers, this review contributes to the ongoing discourse on responsible AI governance and lays the foundation for future research in this critical area. Ultimately, the goal is to promote individual and societal wellbeing by ensuring that AI systems are developed and deployed in a transparent, accountable, and ethical manner.", + "citation_count": 276, + "topics": [ + "Ethics and Social Impacts of AI", + "Artificial Intelligence in Healthcare and Education", + "Explainable Artificial Intelligence (XAI)" + ], + "discovered_via": [ + "query:algorithmic hiring employment discrimination automated decision systems law regulation audit privacy" + ], + "relevance_score": 2.0, + "relevance_reason": "Reviews general AI transparency and accountability rather than employment or hiring-specific systems.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2994948545", + "title": "Considerations for AI fairness for people with disabilities", + "year": 2019, + "url": "https://doi.org/10.1145/3362077.3362086", + "doi": "https://doi.org/10.1145/3362077.3362086", + "abstract": "In society today, people experiencing disability can face discrimination. As artificial intelligence solutions take on increasingly important roles in decision-making and interaction, they have the potential to impact fair treatment of people with disabilities in society both positively and negatively. We describe some of the opportunities and risks across four emerging AI application areas: employment, education, public safety, and healthcare, identified in a workshop with participants experiencing a range of disabilities. In many existing situations, non-AI solutions are already discriminatory, and introducing AI runs the risk of simply perpetuating and replicating these flaws. We next discuss strategies for supporting fairness in the context of disability throughout the AI development lifecycle. AI systems should be reviewed for potential impact on the user in their broader context of use. They should offer opportunities to redress errors, and for users and those impacted to raise fairness concerns. People with disabilities should be included when sourcing data to build models, and in testing, to create a more inclusive and robust system. Finally, we offer pointers into an established body of literature on human-centered design processes and philosophies that may assist AI and ML engineers in innovating algorithms that reduce harm and ultimately enhance the lives of people with disabilities.", + "citation_count": 128, + "topics": [ + "Ethics and Social Impacts of AI", + "Human-Automation Interaction and Safety", + "Technology Use by Older Adults" + ], + "discovered_via": [ + "neighbor:openalex:W4404228668" + ], + "relevance_score": 2.0, + "relevance_reason": "Includes employment among several disability-fairness domains and discusses redress and inclusive testing, but is broad and outside the publication window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W1976888500", + "title": "Hiring as Cultural Matching", + "year": 2012, + "url": "https://doi.org/10.1177/0003122412463213", + "doi": "https://doi.org/10.1177/0003122412463213", + "abstract": "This article presents culture as a vehicle of labor market sorting. Providing a case study of hiring in elite professional service firms, I investigate the often suggested but heretofore empirically unexamined hypothesis that cultural similarities between employers and job candidates matter for employers\u2019 hiring decisions. Drawing from 120 interviews with employers as well as participant observation of a hiring committee, I argue that hiring is more than just a process of skills sorting; it is also a process of cultural matching between candidates, evaluators, and firms. Employers sought candidates who were not only competent but also culturally similar to themselves in terms of leisure pursuits, experiences, and self-presentation styles. Concerns about shared culture were highly salient to employers and often outweighed concerns about absolute productivity. I unpack the interpersonal processes through which cultural similarities affected candidate evaluation in elite firms and provide the first empirical demonstration that shared culture\u2014particularly in the form of lifestyle markers\u2014matters for employer hiring. I conclude by discussing the implications for scholarship on culture, inequality, and labor markets.", + "citation_count": 1180, + "topics": [ + "Social and Cultural Dynamics", + "Gender Diversity and Inequality", + "Migration, Ethnicity, and Economy" + ], + "discovered_via": [ + "neighbor:openalex:W4321105716" + ], + "relevance_score": 1.0, + "relevance_reason": "Examines human cultural matching in hiring, but contains no AI and predates the publication window.", + "heuristic_relevance_score": 1.2, + "heuristic_relevance_reason": "include=none; topic-token-hits=2; exclude=none" + }, + { + "id": "openalex:W1542246681", + "title": "Recruiting Through the Stages: A Meta\u2010Analytic Test of Predictors of Applicant Attraction at Different Stages of the Recruiting Process", + "year": 2012, + "url": "https://doi.org/10.1111/j.1744-6570.2012.01254.x", + "doi": "https://doi.org/10.1111/j.1744-6570.2012.01254.x", + "abstract": "We used meta\u2010analysis and semipartial correlations to examine the relative strength and incremental variance accounted for by 7 categories of recruiting predictors across multiple recruitment stages on applicant attraction. Based on 232 studies (250 samples, 3,518 coefficients, n = 108,632), we found that characteristics of the job, organization, and recruitment process, recruiter behaviors, perceived fit, and hiring expectancies (but not perceived alternatives) accounted for unique variance in applicant attraction at multiple stages. Perceived fit was the strongest relative and unique variance predictor of applicant attraction albeit a nonsignificant predictor of job choice. Although not among the largest zero\u2010order predictors, recruiter behaviors accounted for substantial incremental variance at the first 2 stages. Organizational characteristics are more heavily weighed by applicants when maintaining applicant status as compared to the stage of application, and recruitment process characteristics are weighed progressively more as the recruitment stages advance. Job characteristics accounted for the greatest unique variance in job choice decisions. Job characteristics are more predictive in field studies, whereas recruiter behaviors, recruitment process characteristics, hiring expectancies, and perceived alternatives produced larger effect sizes in the laboratory. Results are discussed in terms of their theoretical and practical implications with future research suggestions.", + "citation_count": 375, + "topics": [ + "Employer Branding and e-HRM", + "Human Resource and Talent Management", + "Job Satisfaction and Organizational Behavior" + ], + "discovered_via": [ + "neighbor:openalex:W4315628734", + "neighbor:openalex:W4318407310" + ], + "relevance_score": 1.0, + "relevance_reason": "Relevant to applicant attraction across recruitment stages, but contains no AI and predates the publication window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2507358938", + "title": "Algorithmic Bias", + "year": 2016, + "url": "https://doi.org/10.1145/2939672.2945386", + "doi": "https://doi.org/10.1145/2939672.2945386", + "abstract": "Algorithms and decision making based on Big Data have become pervasive in all aspects of our daily lives lives (offline and online), as they have become essential tools in personal finance, health care, hiring, housing, education, and policies. It is therefore of societal and ethical importance to ask whether these algorithms can be discriminative on grounds such as gender, ethnicity, or health status. It turns out that the answer is positive: for instance, recent studies in the context of online advertising show that ads for high-income jobs are presented to men much more often than to women [Datta et al., 2015]; and ads for arrest records are significantly more likely to show up on searches for distinctively black names [Sweeney, 2013]. This algorithmic bias exists even when there is no discrimination intention in the developer of the algorithm. Sometimes it may be inherent to the data sources used (software making decisions based on data can reflect, or even amplify, the results of historical discrimination), but even when the sensitive attributes have been suppressed from the input, a well trained machine learning algorithm may still discriminate on the basis of such sensitive attributes because of correlations existing in the data. These considerations call for the development of data mining systems which are discrimination-conscious by-design. This is a novel and challenging research area for the data mining community.", + "citation_count": 343, + "topics": [ + "Ethics and Social Impacts of AI", + "Privacy-Preserving Technologies in Data", + "Artificial Intelligence in Healthcare and Education" + ], + "discovered_via": [ + "neighbor:openalex:W4381848566" + ], + "relevance_score": 1.0, + "relevance_reason": "Mentions hiring as one example of algorithmic discrimination and proxy effects, but is broad and outside the publication window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W104159351", + "title": "Making the most of on-line recruiting.", + "year": 2001, + "url": "https://pubmed.ncbi.nlm.nih.gov/11246921", + "doi": null, + "abstract": "Ninety percent of large U.S. companies are already recruiting via the Internet. By simply logging on to the Web, company recruiters can locate vast numbers of qualified candidates for jobs at every level, screen them in minutes, and contact the most promising ones immediately. The payoffs can be enormous: it costs substantially less to hire someone on-line, and the time saved is equally great. In this article, Peter Cappelli examines some of the emerging service providers and technologies--matchmakers, job boards, hiring management systems software, and applicant-screening mechanisms that test skills and record interests. He also looks at some of the strategies companies are adopting as they enter on-line labor markets. Recruiting needs to be refashioned to resemble marketing, he stresses. Accordingly, smart companies are designing Web pages, and even product ads, with potential recruits in mind. They're giving line managers authority to hire so that candidates in cyberspace aren't lost. They're building internal on-line job networks to retain talent. Integrating recruiting efforts with overall marketing campaigns, especially through coordination and identification with the company's brand, is the most important thing companies can do to ensure success in on-line hiring. Along the way, Cappelli sounds two cautionary notes. First, a human touch, not electronic contact, is vital in the last steps of a successful hiring process. Second, companies must make sure that on-line testing and hiring criteria do not discriminate against women, disabled people, workers over 40, or members of minority groups. When competition for talent is fierce, companies that master the art and science of on-line recruiting will be the ones that attract and keep the best people.", + "citation_count": 284, + "topics": [ + "Employer Branding and e-HRM" + ], + "discovered_via": [ + "neighbor:openalex:W4394590365" + ], + "relevance_score": 1.0, + "relevance_reason": "Discusses online recruiting efficiency, screening, human involvement, and discrimination, but not modern AI and far predates the window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W1491998907", + "title": "E-recruitment: Towards an Ubiquitous Recruitment Process and Candidate Relationship Management", + "year": 2012, + "url": "https://doi.org/10.1177/239700221202600303", + "doi": "https://doi.org/10.1177/239700221202600303", + "abstract": "Up to now, there has been little research on the impact of e-recruitment on the recruitment process as a whole. The present study fills part of this gap by investigating the effect of e-recruitment on the design of the recruitment process. Three explorative case studies were carried out in three large organisations in Denmark in 2008\u20132010. The findings indicate that e-recruitment transforms the traditional recruitment process into a time- and space-independent, collaborative hiring process. The most significant changes are recorded in the sequence and increased divisibility of main recruitment tasks and subtasks. For management, the main task is now that of communicating with candidates. In addition, a new on-going task of maintaining a corporate career website has become an integral part of the new recruitment process. The new design is presented in the following, and its implications briefly discussed.", + "citation_count": 80, + "topics": [ + "Employer Branding and e-HRM", + "Human Resource and Talent Management", + "Management and Organizational Practices" + ], + "discovered_via": [ + "neighbor:openalex:W4390880618" + ], + "relevance_score": 1.0, + "relevance_reason": "Examines how e-recruitment changes hiring workflow and candidate communication, but does not concern AI and predates the window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W4391652653", + "title": "Methodological Approach to Assessing the Current State of Organizations for AI-Based Digital Transformation", + "year": 2024, + "url": "https://doi.org/10.3390/asi7010014", + "doi": "https://doi.org/10.3390/asi7010014", + "abstract": "In an era defined by technological disruption, the integration of artificial intelligence (AI) into business processes is both strategic and challenging. As AI continues to disrupt and reshape industries and revolutionize business processes, organizations must take proactive steps to assess their readiness and capabilities to effectively leverage AI technologies. This research focuses on the assessment elements required to evaluate an organization\u2019s current state in preparation for AI-based digital transformation. This research is based on a literature review and practical insights derived from extensive experience in industrial system engineering. This paper outlines the key assessment elements that organizations should consider to ensure successful and sustainable AI-based digital transformation. This emphasizes the need for a comprehensive approach to assess the organization\u2019s data infrastructure, governance practices, and existing AI capabilities. Furthermore, the research work focuses on the evaluation of AI talent and skills within the organization, considering the significance of fostering an innovative culture and addressing change management challenges. The results of this study provide organizations with elements to assess their current state for AI-based digital transformation. By adopting and implementing the proposed guidelines, organizations can gain a holistic perspective of their current standing, identify strategic opportunities for AI integration, mitigate potential risks, and strategize a successful path forwards in the evolving landscape of AI-driven digital transformation.", + "citation_count": 67, + "topics": [ + "Economic and Technological Systems Analysis", + "Economic and Technological Developments in Russia", + "Economic Development and Digital Transformation" + ], + "discovered_via": [ + "query:AI recruiting business value time to hire cost per hire quality of hire recruiter productivity ROI" + ], + "relevance_score": 1.0, + "relevance_reason": "Discusses organizational AI readiness, data infrastructure, and governance generally, without application to hiring.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2512694330", + "title": "Talent development gamification in talent selection assessment centres", + "year": 2016, + "url": "https://doi.org/10.1108/ejtd-03-2016-0017", + "doi": "https://doi.org/10.1108/ejtd-03-2016-0017", + "abstract": "Purpose The purpose of this paper is to examine the relationship between the use of sophisticated talent selection processes such as gamification and training and development interventions designed to ensure that candidates can successfully navigate the talent assessment process. Gamification is the application of game elements to non-game activities through the adoption of gaming tools, and little is known about how candidates (\u201ctalent\u201d) struggle to learn about the structural mechanics of gamification as they engage with the hidden rules of talent selection, such as goals, rules, \u201clevelling up\u201d, feedback and engagement in competitive \u2013 collaborative activities. The term \u201ctalent development gamification\u201d is coined and used as an analytical tool to consider how young talent are supported by development interventions in their inter-subjectivity as they learn how to survive and win in talent selection games. Design/methodology/approach Studying hidden dynamics in development processes inherent in gamified talent selection is challenging, so a cult work of fiction, \u201cEnder\u2019s Game\u201d, is examined to address the questions: \u201cHow do candidates in talent selection programmes learn to make sense of the structural mechanics of gamification\u201d, \u201cHow does this make the hidden rules of talent selection explicit to them?\u201d and \u201cWhat does this mean for talent development?\u201d Findings Talent development in selection gamification processes is illustrated through nuanced theoretical accounts of how a multiplicity of shifting and competing developmental learning opportunities are played out as a form of \u201cdouble-consciousness\u201d by potential organizational talent for them to \u201cwin the selection game\u201d. Research limitations/implications Using novels as an aid to understanding management and the organization of work is ontologically and epistemologically problematic. But analysing novels which are \u201cgood reads\u201d also has educational value and can produce new knowledge from its analysis. In exploring how \u201cCharacters are made to live dangerously, to face predicaments that, as readers, we experience as vicarious pleasure. We imagine, for example, how a particular character may react or, more importantly, what we would do in similar circumstances\u201d (Knights and Willmott, 1999, p. 5). This future-oriented fictional narrative is both illustrative and provides an analogy to illuminate current organisational development challenges. Originality/value The term \u201ctalent development gamification in selection processes\u201d is coined to allow analysis and provide lessons for talent development practice in a little studied area. Our case study analysis identifies a number of areas for consideration by talent management/talent development specialists involved in developing talent assessment centres incorporating gamification. These include the importance of understanding and taking account of rites of passage through the assessment centre, in particular the role of liminal space, what talent development interventions might be of benefit and the necessity of appreciating and managing talent in developing the skill of double consciousness in game simulations.", + "citation_count": 38, + "topics": [ + "Educational Games and Gamification", + "Digital Games and Media", + "Human Resource and Talent Management" + ], + "discovered_via": [ + "neighbor:openalex:W4381684347" + ], + "relevance_score": 1.0, + "relevance_reason": "Concerns gamified talent assessment and candidate gaming, but not AI and outside the publication window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W1901676042", + "title": "Simply the best? The case for using \u2018threshold selection\u2019 in hiring decisions", + "year": 2011, + "url": "https://doi.org/10.1111/j.1748-8583.2011.00168.x", + "doi": "https://doi.org/10.1111/j.1748-8583.2011.00168.x", + "abstract": "This paper argues the case for a more progressive approach to diversity and inclusion that incorporates a form of positive discrimination in the selection process: \u2018threshold selection\u2019. It outlines the distinctive features of threshold selection and argues that it is an approach that resolves the tension between suitability and acceptability criteria, relies on high formalisation and transparency, can mitigate some of the self\u2010serving bias among selectors and ties the selection needs to the goals of the organisation. In these respects, although controversial, threshold selection offers a thorough and systematic approach that is entirely consistent with the general tendency towards greater professionalisation of HRM.", + "citation_count": 33, + "topics": [ + "Gender Diversity and Inequality", + "Labor market dynamics and wage inequality", + "Employer Branding and e-HRM" + ], + "discovered_via": [ + "neighbor:openalex:W4366989571" + ], + "relevance_score": 1.0, + "relevance_reason": "Discusses transparent threshold-based human selection and diversity, but contains no AI and predates the window.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W2067108088", + "title": "Perceiver self-presentational goals as moderators of expectancy influences: Ingratiation and the disconfirmation of negative expectancies.", + "year": 1993, + "url": "https://doi.org/10.1037//0022-3514.64.3.409", + "doi": "https://doi.org/10.1037//0022-3514.64.3.409", + "abstract": "Self-fulfilling prophecy processes enable people to confirm their negative expectancies for others. The perceiver goal of ingratiation was hypothesized to alter this behavioral dynamic and thus lead perceivers to disconfirm their negative expectancies. In an interview setting, we manipulated interviewer Ss' expectancies and interaction goals. As anticipated, \"no goal\" interviewers were relatively cold and challenging toward their negative-expectancy applicants; as a result, these applicants performed somewhat less favorably, consistent with interviewer expectancies. In contrast, \"liking goal\" interviewers were relatively warm and unthreatening toward their negative-expectancy applicants; as a result, these applicants performed favorably, disconfirming interviewer expectancies. These data support a framework in which perceiver self-presentation goals are conceptualized to moderate the expectancy-confirmation process.", + "citation_count": 25, + "topics": [ + "Social and Intergroup Psychology", + "Personality Traits and Psychology", + "Behavioral Health and Interventions" + ], + "discovered_via": [ + "neighbor:openalex:W4394590365" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none" + }, + { + "id": "openalex:W3203196106", + "title": "Screened Out Onscreen: Disability Discrimination, Hiring Bias, and Artificial Intelligence", + "year": 2021, + "url": "https://doi.org/10.2139/ssrn.3906300", + "doi": "https://doi.org/10.2139/ssrn.3906300", + "abstract": "", + "citation_count": 24, + "topics": [ + "Digital Economy and Work Transformation", + "Legal and Policy Issues", + "Retirement, Disability, and Employment" + ], + "discovered_via": [ + "neighbor:openalex:W4404228668" + ], + "relevance_score": 0.6, + "relevance_reason": "include=none; 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Browse category pages, the candidate queue, or follow related-paper trails.", + "links": [ + "category:Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication", + "category:AI function: generation, prediction, ranking, classification, recommendation, conversation, workflow automation", + "category:Decision role: assistive, advisory, semi-automated, autonomous", + "category:Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security", + "category:Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor", + "category:Risk mechanism: biased data, proxy variables, measurement error, distribution shift, gaming, hallucination, automation bias, weak oversight", + "category:Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance" + ] + }, + { + "id": "candidate-queue", + "type": "queue", + "title": "Candidate Queue", + "subtitle": "25 papers awaiting review", + "body": "Top discovered papers that have not been accepted into the corpus yet.", + "links": [ + "candidate:openalex:W4409825334", + "candidate:openalex:W4404228668", + "candidate:openalex:W4394590365", + "candidate:openalex:W4386714740", + "candidate:openalex:W4391598857", + "candidate:openalex:W4366989571", + "candidate:openalex:W4390880618", + "candidate:openalex:W4315628734", + "candidate:openalex:W4288083801", + "candidate:openalex:W4382520149", + "candidate:openalex:W4383913712", + "candidate:openalex:W2072937224", + "candidate:openalex:W4381848566", + "candidate:openalex:W4400303336", + "candidate:openalex:W2994948545", + "candidate:openalex:W1976888500", + "candidate:openalex:W1542246681", + "candidate:openalex:W2507358938", + "candidate:openalex:W104159351", + "candidate:openalex:W1491998907", + "candidate:openalex:W4391652653", + "candidate:openalex:W2512694330", + "candidate:openalex:W1901676042", + "candidate:openalex:W2067108088", + "candidate:openalex:W3203196106" + ] + }, + { + "id": "category:Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication", + "type": "category", + "title": "Hiring Stage: Sourcing, Matching, Screening, Assessment, Interviewing, Selection, Communication", + "subtitle": "4 accepted papers", + "body": "Generated category page for Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication.", + "links": [ + "openalex:1", + "openalex:W4409825334", + "openalex:W4404228668", + "openalex:W4391598857" + ] + }, + { + "id": "category:AI function: generation, prediction, ranking, classification, recommendation, conversation, workflow automation", + "type": "category", + "title": "Ai Function: Generation, Prediction, Ranking, Classification, Recommendation, Conversation, Workflow Automation", + "subtitle": "0 accepted papers", + "body": "Generated category page for AI function: generation, prediction, ranking, classification, recommendation, conversation, workflow automation.", + "links": [] + }, + { + "id": "category:Decision role: assistive, advisory, semi-automated, autonomous", + "type": "category", + "title": "Decision Role: Assistive, Advisory, Semi-Automated, Autonomous", + "subtitle": "0 accepted papers", + "body": "Generated category page for Decision role: assistive, advisory, semi-automated, autonomous.", + "links": [] + }, + { + "id": "category:Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security", + "type": "category", + "title": "Evaluation Dimension: Utility, Validity, Reliability, Efficiency, Fairness, Privacy, Transparency, Accessibility, Security", + "subtitle": "1 accepted papers", + "body": "Generated category page for Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security.", + "links": [ + "openalex:W4394590365" + ] + }, + { + "id": "category:Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor", + "type": "category", + "title": "Stakeholder: Applicant, Recruiter, Hiring Manager, Employer, Vendor, Regulator, Auditor", + "subtitle": "2 accepted papers", + "body": "Generated category page for Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor.", + "links": [ + "openalex:W4386714740", + "openalex:W4366989571" + ] + }, + { + "id": "category:Risk mechanism: biased data, proxy variables, measurement error, distribution shift, gaming, hallucination, automation bias, weak oversight", + "type": "category", + "title": "Risk Mechanism: Biased Data, Proxy Variables, Measurement Error, Distribution Shift, Gaming, Hallucination, Automation Bias, Weak Oversight", + "subtitle": "0 accepted papers", + "body": "Generated category page for Risk mechanism: biased data, proxy variables, measurement error, distribution shift, gaming, hallucination, automation bias, weak oversight.", + "links": [] + }, + { + "id": "category:Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance", + "type": "category", + "title": "Governance Control: Documentation, Consent, Human Review, Appeal, Audit, Monitoring, Incident Response, Vendor Assurance", + "subtitle": "1 accepted papers", + "body": "Generated category page for Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance.", + "links": [ + "openalex:W4390880618" + ] + }, + { + "id": "openalex:1", + "type": "paper", + "title": "Formal proof search", + "subtitle": "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication \u00b7 2025", + "body": "Proof search with verifier feedback.", + "source_url": "https://example.com", + "assessment": "aligned", + "related": [ + { + "id": "openalex:W4404228668", + "title": "Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE)", + "reason": "same category; terms: feedback" + } + ], + "links": [ + "category:Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication" + ] + }, + { + "id": "openalex:W4409825334", + "type": "paper", + "title": "Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity", + "subtitle": "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication \u00b7 2025", + "body": "This paper explores the efficacy of AI-driven chatbots in accurately inferring personality traits compared to traditional psychometric tests within a real-world professional hiring context. The study is driven by the increasing integration of AI tools in recruitment processes, which necessitates a deeper understanding of their reliability and validity. Using a quasi-experimental design with propensity score matching, we analysed data from 159 candidates and other professionals from Serbian and Montenegrin regions who completed both traditional psychometric assessments and AI-based personality evaluations based on the Big Five Personality model. A novel one-question-per-facet approach was employed in the chatbot assessments with a goal of enabling more granular analysis of the chatbot's psychometric properties. The findings indicate that the chatbot demonstrated good structural, substantive, and convergent validity for certain traits, particularly Extraversion and Conscientiousness, but not for Neuroticism, Agreeableness, and Openness. While robust regression confirmed that AI-inferred scores are less susceptible to social desirability bias than traditional tests, they did not significantly predict real-world outcomes, indicating issues with external validity, particularly predictive validity. The results suggest that AI-driven chatbots show promise for identifying certain personality traits and demonstrate resistance to social desirability bias. This paper contributes to the emerging field of AI and psychometrics by offering insights into the potential and limitations of AI tools in professional selection, while developing an approach for refining psychometric properties of AI-driven assessments.", + "source_url": "https://doi.org/10.3389/fpsyg.2025.1564979", + "assessment": "Direct hiring-context comparison of AI chatbot assessments and psychometric tests, with concrete evidence on validity, reliability, predictive performance, and social-desirability bias.", + "related": [ + { + "id": "openalex:W4391598857", + "title": "A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring", + "reason": "same category; terms: ai-driven, bias, contributes" + }, + { + "id": "openalex:W4390880618", + "title": "A UTAUT-Based Framework for Analyzing Users\u2019 Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand", + "reason": "terms: branding, context, e-hrm" + }, + { + "id": "openalex:W4366989571", + "title": "Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring", + "reason": "terms: assessments, branding, contributes" + }, + { + "id": "openalex:W4386714740", + "title": "Ethics and discrimination in artificial intelligence-enabled recruitment practices", + "reason": "terms: ai-driven, analysis, branding" + }, + { + "id": "openalex:W4404228668", + "title": "Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE)", + "reason": "same category; terms: analysis, bias, hiring" + }, + { + "id": "openalex:W4394590365", + "title": "Fairness, AI & recruitment", + "reason": "terms: branding, candidates, certain" + } + ], + "links": [ + "category:Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication" + ] + }, + { + "id": "openalex:W4404228668", + "type": "paper", + "title": "Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE)", + "subtitle": "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication \u00b7 2024", + "body": "IntroductionThis research aims to explore the effectiveness and inclusivity of AI-powered recruitment tools in hiring people with disabilities within the United Arab Emirates. Such is the situation where AI integration into the arena of recruitment is increasingly rapid, while there are vital issues on the side of bias, accessibility, and fairness for applicants of diverse needs. MethodsThis study was a mixed-methods approach, examining sentiment analysis, emotion detection, and HR analytics of feedback from applicants with a disability, 415 in total. The research focused on scores referring to sentiment, the progression rate, and the outcome of the final hiring. ResultsThe sentiment score varied significantly across disability types (p-value <0.05). The applicants with cognitive disability expressed the highest sentiment sore while applicants with hearing impairment had the lowest, which indicated the varying adaptability of AI. The emotion analysis depicted a mix of positive and negative emotions. A few applicants liked technology and have trust in it, while others report fear. Clearly, the applicants, both disabled and non-disabled did not differ in their rate of progression (p-value >0.05), hence never indicating any significant difference within the initial steps of the process. The final hiring stage showed significant differences in results with (p-value <0.05), where the proportionate number of disabled applicants was recorded to be lower than that of non-disabled applicants", + "source_url": "https://doi.org/10.56294/dm2025487", + "assessment": "Direct mixed-methods evaluation of accessibility, fairness, applicant experience, progression, and hiring outcomes for disabled applicants using AI recruitment tools in the UAE.", + "related": [ + { + "id": "openalex:W4391598857", + "title": "A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring", + "reason": "same category; terms: bias, fairness, hiring" + }, + { + "id": "openalex:W4390880618", + "title": "A UTAUT-Based Framework for Analyzing Users\u2019 Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand", + "reason": "terms: integration, organizational, performance" + }, + { + "id": "openalex:W4409825334", + "title": "Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity", + "reason": "same category; terms: analysis, bias, hiring" + }, + { + "id": "openalex:W4394590365", + "title": "Fairness, AI & recruitment", + "reason": "terms: applicants, evaluating, fairness" + }, + { + "id": "openalex:1", + "title": "Formal proof search", + "reason": "same category; terms: feedback" + } + ], + "links": [ + "category:Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication" + ] + }, + { + "id": "openalex:W4394590365", + "type": "paper", + "title": "Fairness, AI & recruitment", + "subtitle": "Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security \u00b7 2024", + "body": "The ever-increasing adoption of AI technologies in the hiring landscape to enhance human resources efficiency raises questions about algorithmic decision-making's implications in employment, especially for job applicants, including those at higher risk of social discrimination. Among other concepts, such as transparency and accountability, fairness has become crucial in AI recruitment debates due to the potential reproduction of bias and discrimination that can disproportionately affect certain vulnerable groups. However, the ideals and ambitions of fairness may signify different meanings to various stakeholders. Conceptualizing fairness is critical because it may provide a clear benchmark for evaluating and mitigating biases, ensuring that AI systems do not perpetuate existing imbalances and promote, in this case, equitable opportunities for all candidates in the job market. To this end, in this article, we conduct a scoping literature review on fairness in AI applications for recruitment and selection purposes, with special emphasis on its definition, categorization, and practical implementation. We start by explaining how AI applications have been increasingly used in the hiring process, especially to increase the efficiency of the HR team. We then move to the limitations of this technological innovation, which is known to be at high risk of privacy violations and social discrimination. Against this backdrop, we focus on defining and operationalizing fairness in AI applications for recruitment and selection purposes through cross-disciplinary lenses. Although the applicable legal frameworks and some research currently address the issue piecemeal, we observe and welcome the emergence of some cross-disciplinary efforts aimed at tackling this multifaceted challenge. We conclude the article with some brief recommendations to guide and shape future research and action on the fairness of AI applications in the hiring process for the better.", + "source_url": "https://doi.org/10.1016/j.clsr.2024.105966", + "assessment": "Direct scoping review of fairness definitions and implementation in AI recruitment and selection, including bias, vulnerable groups, privacy, transparency, accountability, and legal frameworks.", + "related": [ + { + "id": "openalex:W4391598857", + "title": "A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring", + "reason": "terms: algorithmic, biases, efficiency" + }, + { + "id": "openalex:W4390880618", + "title": "A UTAUT-Based Framework for Analyzing Users\u2019 Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand", + "reason": "terms: adoption, branding, currently" + }, + { + "id": "openalex:W4366989571", + "title": "Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring", + "reason": "terms: algorithmic, although, article" + }, + { + "id": "openalex:W4409825334", + "title": "Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity", + "reason": "terms: branding, candidates, certain" + }, + { + "id": "openalex:W4386714740", + "title": "Ethics and discrimination in artificial intelligence-enabled recruitment practices", + "reason": "terms: address, algorithmic, applications" + }, + { + "id": "openalex:W4404228668", + "title": "Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE)", + "reason": "terms: applicants, evaluating, fairness" + } + ], + "links": [ + "category:Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security" + ] + }, + { + "id": "openalex:W4386714740", + "type": "paper", + "title": "Ethics and discrimination in artificial intelligence-enabled recruitment practices", + "subtitle": "Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor \u00b7 2023", + "body": "Abstract This study aims to address the research gap on algorithmic discrimination caused by AI-enabled recruitment and explore technical and managerial solutions. The primary research approach used is a literature review. The findings suggest that AI-enabled recruitment has the potential to enhance recruitment quality, increase efficiency, and reduce transactional work. However, algorithmic bias results in discriminatory hiring practices based on gender, race, color, and personality traits. The study indicates that algorithmic bias stems from limited raw data sets and biased algorithm designers. To mitigate this issue, it is recommended to implement technical measures, such as unbiased dataset frameworks and improved algorithmic transparency, as well as management measures like internal corporate ethical governance and external oversight. Employing Grounded Theory, the study conducted survey analysis to collect firsthand data on respondents\u2019 experiences and perceptions of AI-driven recruitment applications and discrimination.", + "source_url": "https://doi.org/10.1057/s41599-023-02079-x", + "assessment": "Directly addresses AI recruitment efficiency, discriminatory outcomes, biased data and design, transparency, governance, and external oversight using review and survey evidence.", + "related": [ + { + "id": "openalex:W4366989571", + "title": "Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring", + "reason": "same category; terms: abstract, algorithmic, artificial" + }, + { + "id": "openalex:W4409825334", + "title": "Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity", + "reason": "terms: ai-driven, analysis, branding" + }, + { + "id": "openalex:W4394590365", + "title": "Fairness, AI & recruitment", + "reason": "terms: address, algorithmic, applications" + } + ], + "links": [ + "category:Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor" + ] + }, + { + "id": "openalex:W4391598857", + "type": "paper", + "title": "A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring", + "subtitle": "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication \u00b7 2024", + "body": "The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms\u2019 fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society.", + "source_url": "https://doi.org/10.3390/ai5010019", + "assessment": "Direct systematic review of bias and mitigation in AI-based CV screening, including efficiency, fairness techniques, case studies, and human\u2013machine collaboration.", + "related": [ + { + "id": "openalex:W4409825334", + "title": "Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity", + "reason": "same category; terms: ai-driven, bias, contributes" + }, + { + "id": "openalex:W4404228668", + "title": "Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE)", + "reason": "same category; terms: bias, fairness, hiring" + }, + { + "id": "openalex:W4394590365", + "title": "Fairness, AI & recruitment", + "reason": "terms: algorithmic, biases, efficiency" + } + ], + "links": [ + "category:Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication" + ] + }, + { + "id": "openalex:W4366989571", + "type": "paper", + "title": "Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring", + "subtitle": "Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor \u00b7 2023", + "body": "Abstract Despite frequent claims that increased use of artificial intelligence (AI) in hiring will reduce the human bias that has long plagued recruitment and selection, AI may equally replicate and amplify such bias and embed it in technology. This article explores exclusion and inclusion in AI\u2010supported hiring, focusing on three interrelated areas: data, design and decisions. It is suggested that in terms of data, organisational fit, categorisations and intersectionality require consideration in relation to exclusion. As various stakeholders collaborate to create AI, it is essential to explore which groups are dominant and how subjective assessments are encoded in technology. Although AI\u2010supported hiring should enhance recruitment decisions, evidence is lacking on how humans and machines interact in decision\u2010making, and how algorithms can be audited and regulated effectively for inclusion. This article recommends areas for interrogation through further research, and contributes to understanding how algorithmic inclusion can be achieved in AI\u2010supported hiring.", + "source_url": "https://doi.org/10.1111/1748-8583.12511", + "assessment": "Directly analyzes bias, inclusion, data, design, human\u2013AI decisions, auditing, and regulation in AI-supported hiring, though it mainly proposes research directions rather than outcome evidence.", + "related": [ + { + "id": "openalex:W4409825334", + "title": "Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity", + "reason": "terms: assessments, branding, contributes" + }, + { + "id": "openalex:W4386714740", + "title": "Ethics and discrimination in artificial intelligence-enabled recruitment practices", + "reason": "same category; terms: abstract, algorithmic, artificial" + }, + { + "id": "openalex:W4394590365", + "title": "Fairness, AI & recruitment", + "reason": "terms: algorithmic, although, article" + } + ], + "links": [ + "category:Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor" + ] + }, + { + "id": "openalex:W4390880618", + "type": "paper", + "title": "A UTAUT-Based Framework for Analyzing Users\u2019 Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand", + "subtitle": "Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance \u00b7 2024", + "body": "Recruitment is a fundamental aspect of Human Resource Management to drive organizational performance. Traditional recruitment processes, with manual stages, are time-consuming and inefficient. Artificial Intelligence (AI), which demonstrates its potential in various sectors such as healthcare, education, and notable cases of ChatGPT, is currently reshaping recruitment by automating tasks to improve efficiency. However, in Thailand, where there is a growing demand for talents, the application of AI in recruitment remains relatively limited. This study focuses on human resources (HR) and recruitment professionals in Thailand, aiming to understand their perspectives on the integration of AI in recruitment. It utilized the Unified Theory for Acceptance and Use of Technology (UTAUT) model, customized to suit the specific requirements of Thailand recruitment practices. The study explores the factors influencing users\u2019 intention to adopt AI in recruitment. Survey questionnaire items were created based on prior literature and refined with insights from HR and recruitment experts to ensure applicability in the context of recruitment in Thailand. A survey involving 364 HR and recruiting professionals in the Bangkok metropolitan area supplied comprehensive responses. The study reveals that several factors, including perceived value, perceived autonomy, effort expectancy, and facilitating conditions, significantly impact the intention to adopt AI for recruitment. While social influence and trust in AI technology do not have a direct influence on intention, social influence directly affects perceived value. Trust in AI technology positively influences Effort Expectancy. This study provides valuable benefits for HR and recruitment professionals, organizations, and AI developers by offering insights into AI adoption and sustainability, enhancing recruitment processes and promoting the effective use of AI tools in this sector.", + "source_url": "https://doi.org/10.3390/systems12010028", + "assessment": "Provides hiring-specific survey evidence from Thailand on recruiter adoption of AI, including value, autonomy, effort, facilitating conditions, and trust.", + "related": [ + { + "id": "openalex:W4409825334", + "title": "Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity", + "reason": "terms: branding, context, e-hrm" + }, + { + "id": "openalex:W4404228668", + "title": "Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE)", + "reason": "terms: integration, organizational, performance" + }, + { + "id": "openalex:W4394590365", + "title": "Fairness, AI & recruitment", + "reason": "terms: adoption, branding, currently" + } + ], + "links": [ + "category:Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance" + ] + }, + { + "id": "candidate:openalex:W4409825334", + "type": "candidate", + "title": "Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity", + "subtitle": "pending review \u00b7 2025", + "body": "This paper explores the efficacy of AI-driven chatbots in accurately inferring personality traits compared to traditional psychometric tests within a real-world professional hiring context. The study is driven by the increasing integration of AI tools in recruitment processes, which necessitates a deeper understanding of their reliability and validity. Using a quasi-experimental design with propensity score matching, we analysed data from 159 candidates and other professionals from Serbian and Montenegrin regions who completed both traditional psychometric assessments and AI-based personality evaluations based on the Big Five Personality model. A novel one-question-per-facet approach was employed in the chatbot assessments with a goal of enabling more granular analysis of the chatbot's psychometric properties. The findings indicate that the chatbot demonstrated good structural, substantive, and convergent validity for certain traits, particularly Extraversion and Conscientiousness, but not for Neuroticism, Agreeableness, and Openness. While robust regression confirmed that AI-inferred scores are less susceptible to social desirability bias than traditional tests, they did not significantly predict real-world outcomes, indicating issues with external validity, particularly predictive validity. The results suggest that AI-driven chatbots show promise for identifying certain personality traits and demonstrate resistance to social desirability bias. This paper contributes to the emerging field of AI and psychometrics by offering insights into the potential and limitations of AI tools in professional selection, while developing an approach for refining psychometric properties of AI-driven assessments.", + "source_url": "https://doi.org/10.3389/fpsyg.2025.1564979", + "assessment": "Directly evaluates an AI hiring assessment against psychometric tests using reliability, convergent validity, predictive validity, and social-desirability outcomes.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W4404228668", + "type": "candidate", + "title": "Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE)", + "subtitle": "pending review \u00b7 2024", + "body": "IntroductionThis research aims to explore the effectiveness and inclusivity of AI-powered recruitment tools in hiring people with disabilities within the United Arab Emirates. Such is the situation where AI integration into the arena of recruitment is increasingly rapid, while there are vital issues on the side of bias, accessibility, and fairness for applicants of diverse needs. MethodsThis study was a mixed-methods approach, examining sentiment analysis, emotion detection, and HR analytics of feedback from applicants with a disability, 415 in total. The research focused on scores referring to sentiment, the progression rate, and the outcome of the final hiring. ResultsThe sentiment score varied significantly across disability types (p-value <0.05). The applicants with cognitive disability expressed the highest sentiment sore while applicants with hearing impairment had the lowest, which indicated the varying adaptability of AI. The emotion analysis depicted a mix of positive and negative emotions. A few applicants liked technology and have trust in it, while others report fear. Clearly, the applicants, both disabled and non-disabled did not differ in their rate of progression (p-value >0.05), hence never indicating any significant difference within the initial steps of the process. The final hiring stage showed significant differences in results with (p-value <0.05), where the proportionate number of disabled applicants was recorded to be lower than that of non-disabled applicants", + "source_url": "https://doi.org/10.56294/dm2025487", + "assessment": "Direct mixed-methods evaluation of AI recruitment accessibility, fairness, candidate experience, progression, and hiring outcomes for applicants with disabilities in the UAE.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W4394590365", + "type": "candidate", + "title": "Fairness, AI & recruitment", + "subtitle": "pending review \u00b7 2024", + "body": "The ever-increasing adoption of AI technologies in the hiring landscape to enhance human resources efficiency raises questions about algorithmic decision-making's implications in employment, especially for job applicants, including those at higher risk of social discrimination. Among other concepts, such as transparency and accountability, fairness has become crucial in AI recruitment debates due to the potential reproduction of bias and discrimination that can disproportionately affect certain vulnerable groups. However, the ideals and ambitions of fairness may signify different meanings to various stakeholders. Conceptualizing fairness is critical because it may provide a clear benchmark for evaluating and mitigating biases, ensuring that AI systems do not perpetuate existing imbalances and promote, in this case, equitable opportunities for all candidates in the job market. To this end, in this article, we conduct a scoping literature review on fairness in AI applications for recruitment and selection purposes, with special emphasis on its definition, categorization, and practical implementation. We start by explaining how AI applications have been increasingly used in the hiring process, especially to increase the efficiency of the HR team. We then move to the limitations of this technological innovation, which is known to be at high risk of privacy violations and social discrimination. Against this backdrop, we focus on defining and operationalizing fairness in AI applications for recruitment and selection purposes through cross-disciplinary lenses. Although the applicable legal frameworks and some research currently address the issue piecemeal, we observe and welcome the emergence of some cross-disciplinary efforts aimed at tackling this multifaceted challenge. We conclude the article with some brief recommendations to guide and shape future research and action on the fairness of AI applications in the hiring process for the better.", + "source_url": "https://doi.org/10.1016/j.clsr.2024.105966", + "assessment": "Hiring-specific scoping review addressing fairness definitions, operationalization, bias, discrimination, privacy, transparency, accountability, and legal frameworks.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W4386714740", + "type": "candidate", + "title": "Ethics and discrimination in artificial intelligence-enabled recruitment practices", + "subtitle": "pending review \u00b7 2023", + "body": "Abstract This study aims to address the research gap on algorithmic discrimination caused by AI-enabled recruitment and explore technical and managerial solutions. The primary research approach used is a literature review. The findings suggest that AI-enabled recruitment has the potential to enhance recruitment quality, increase efficiency, and reduce transactional work. However, algorithmic bias results in discriminatory hiring practices based on gender, race, color, and personality traits. The study indicates that algorithmic bias stems from limited raw data sets and biased algorithm designers. To mitigate this issue, it is recommended to implement technical measures, such as unbiased dataset frameworks and improved algorithmic transparency, as well as management measures like internal corporate ethical governance and external oversight. Employing Grounded Theory, the study conducted survey analysis to collect firsthand data on respondents\u2019 experiences and perceptions of AI-driven recruitment applications and discrimination.", + "source_url": "https://doi.org/10.1057/s41599-023-02079-x", + "assessment": "Directly addresses AI recruitment efficiency, discriminatory outcomes, biased data and designers, transparency, governance, and external oversight, though its methods description is unclear.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W4391598857", + "type": "candidate", + "title": "A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring", + "subtitle": "pending review \u00b7 2024", + "body": "The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms\u2019 fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society.", + "source_url": "https://doi.org/10.3390/ai5010019", + "assessment": "Direct review of bias mitigation in AI CV screening, covering efficiency, fairness techniques, case studies, and human-machine collaboration.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W4366989571", + "type": "candidate", + "title": "Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring", + "subtitle": "pending review \u00b7 2023", + "body": "Abstract Despite frequent claims that increased use of artificial intelligence (AI) in hiring will reduce the human bias that has long plagued recruitment and selection, AI may equally replicate and amplify such bias and embed it in technology. This article explores exclusion and inclusion in AI\u2010supported hiring, focusing on three interrelated areas: data, design and decisions. It is suggested that in terms of data, organisational fit, categorisations and intersectionality require consideration in relation to exclusion. As various stakeholders collaborate to create AI, it is essential to explore which groups are dominant and how subjective assessments are encoded in technology. Although AI\u2010supported hiring should enhance recruitment decisions, evidence is lacking on how humans and machines interact in decision\u2010making, and how algorithms can be audited and regulated effectively for inclusion. This article recommends areas for interrogation through further research, and contributes to understanding how algorithmic inclusion can be achieved in AI\u2010supported hiring.", + "source_url": "https://doi.org/10.1111/1748-8583.12511", + "assessment": "Directly analyzes inclusion risks in AI-supported hiring across data, design, human-machine decisions, auditing, and regulation, though it appears primarily conceptual.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W4390880618", + "type": "candidate", + "title": "A UTAUT-Based Framework for Analyzing Users\u2019 Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand", + "subtitle": "pending review \u00b7 2024", + "body": "Recruitment is a fundamental aspect of Human Resource Management to drive organizational performance. Traditional recruitment processes, with manual stages, are time-consuming and inefficient. Artificial Intelligence (AI), which demonstrates its potential in various sectors such as healthcare, education, and notable cases of ChatGPT, is currently reshaping recruitment by automating tasks to improve efficiency. However, in Thailand, where there is a growing demand for talents, the application of AI in recruitment remains relatively limited. This study focuses on human resources (HR) and recruitment professionals in Thailand, aiming to understand their perspectives on the integration of AI in recruitment. It utilized the Unified Theory for Acceptance and Use of Technology (UTAUT) model, customized to suit the specific requirements of Thailand recruitment practices. The study explores the factors influencing users\u2019 intention to adopt AI in recruitment. Survey questionnaire items were created based on prior literature and refined with insights from HR and recruitment experts to ensure applicability in the context of recruitment in Thailand. A survey involving 364 HR and recruiting professionals in the Bangkok metropolitan area supplied comprehensive responses. The study reveals that several factors, including perceived value, perceived autonomy, effort expectancy, and facilitating conditions, significantly impact the intention to adopt AI for recruitment. While social influence and trust in AI technology do not have a direct influence on intention, social influence directly affects perceived value. Trust in AI technology positively influences Effort Expectancy. This study provides valuable benefits for HR and recruitment professionals, organizations, and AI developers by offering insights into AI adoption and sustainability, enhancing recruitment processes and promoting the effective use of AI tools in this sector.", + "source_url": "https://doi.org/10.3390/systems12010028", + "assessment": "Hiring-specific survey of 364 recruitment professionals measuring AI adoption factors including value, autonomy, effort, conditions, and trust in Thailand.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W4315628734", + "type": "candidate", + "title": "Applicants\u2019 Fairness Perceptions of Algorithm-Driven Hiring Procedures", + "subtitle": "pending review \u00b7 2023", + "body": "Abstract unavailable.", + "source_url": "https://doi.org/10.1007/s10551-022-05320-w", + "assessment": "The title directly targets applicant fairness perceptions of algorithm-driven hiring, but the missing abstract prevents assessment of methods and findings.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W4288083801", + "type": "candidate", + "title": "Mitigating bias in algorithmic hiring", + "subtitle": "pending review \u00b7 2020", + "body": "There has been rapidly growing interest in the use of algorithms in hiring, especially as a means to address or mitigate bias. Yet, to date, little is known about how these methods are used in practice. How are algorithmic assessments built, validated, and examined for bias? In this work, we document and analyze the claims and practices of companies offering algorithms for employment assessment. In particular, we identify vendors of algorithmic pre-employment assessments (i.e., algorithms to screen candidates), document what they have disclosed about their development and validation procedures, and evaluate their practices, focusing particularly on efforts to detect and mitigate bias. Our analysis considers both technical and legal perspectives. Technically, we consider the various choices vendors make regarding data collection and prediction targets, and explore the risks and trade-offs that these choices pose. We also discuss how algorithmic de-biasing techniques interface with, and create challenges for, antidiscrimination law.", + "source_url": "https://doi.org/10.1145/3351095.3372828", + "assessment": "Highly relevant analysis of validation, bias, vendor practices, and antidiscrimination law in algorithmic pre-employment screening, but published before 2023.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W4382520149", + "type": "candidate", + "title": "The Impact of AI on Recruitment and Selection Processes: Analysing the role of AI in automating and enhancing recruitment and selection procedures", + "subtitle": "pending review \u00b7 2023", + "body": "Human resource management is the process of identifying, recruiting, hiring, and training talented individuals, as well as providing them with career advancement possibilities and critical feedback on their performance. The purpose of this study was to investigate the function of AI in HRM practises using qualitative bibliometric analysis. Scopus, emerald, and the Jstore library are used as data sources. This analysis contains adjustments to data spanning 18 years. It also showed that there is a constant improvement and introduction of new technological conveniences. In accordance with the present market climate, which promotes and celebrates process management and people management practises targeted at making the organisation economically viable and different from the competition, this is a positive development. This work advances the theoretical understanding of AI's growth in the HR sector in light of this reality. Articles and proceedings examined in this research reveal that different authors and academic institutions provide different perspectives on the problem.", + "source_url": "https://doi.org/10.55938/ijgasr.v2i2.50", + "assessment": "Directly concerns AI in recruitment and selection, but the abstract is a broad bibliometric HRM discussion with little concrete hiring evidence or evaluation.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W4383913712", + "type": "candidate", + "title": "Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT", + "subtitle": "pending review \u00b7 2023", + "body": "Abstract ChatGPT and its variants that use generative artificial intelligence (AI) models have rapidly become a focal point in academic and media discussions about their potential benefits and drawbacks across various sectors of the economy, democracy, society, and environment. It remains unclear whether these technologies result in job displacement or creation, or if they merely shift human labour by generating new, potentially trivial or practically irrelevant, information and decisions. According to the CEO of ChatGPT, the potential impact of this new family of AI technology could be as big as \u201cthe printing press\u201d, with significant implications for employment, stakeholder relationships, business models, and academic research, and its full consequences are largely undiscovered and uncertain. The introduction of more advanced and potent generative AI tools in the AI market, following the launch of ChatGPT, has ramped up the \u201cAI arms race\u201d, creating continuing uncertainty for workers, expanding their business applications, while heightening risks related to well\u2010being, bias, misinformation, context insensitivity, privacy issues, ethical dilemmas, and security. Given these developments, this perspectives editorial offers a collection of perspectives and research pathways to extend HRM scholarship in the realm of generative AI. In doing so, the discussion synthesizes the literature on AI and generative AI, connecting it to various aspects of HRM processes, practices, relationships, and outcomes, thereby contributing to shaping the future of HRM research.", + "source_url": "https://doi.org/10.1111/1748-8583.12524", + "assessment": "Discusses generative AI across HRM and relevant risks such as bias and privacy, but the abstract does not establish a substantive hiring-specific analysis.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W2072937224", + "type": "candidate", + "title": "Efficient multifaceted screening of job applicants", + "subtitle": "pending review \u00b7 2013", + "body": "Built on top of human resources management databases within the enterprise, we present a decision support system for managing and optimizing screening activities during the hiring process in a large organization. The basic idea is to prioritize the efforts of human resource practitioners to focus on candidates that are likely of high quality, that are likely to accept a job offer if made one, and that are likely to remain with the organization for the long term. To do so, the system first individually ranks candidates along several dimensions using a keyword matching algorithm and several bipartite ranking algorithms with univariate loss trained on historical actions. Next, individual rankings are aggregated to derive a single list that is presented to the recruitment team through an interactive portal. The portal supports multiple filters that facilitate effective identification of candidates. We demonstrate the usefulness of our system on data collected from a large organization over several years with business value metrics showing greater hiring yield with less interviews. Similarly, using historical pre-hire data we demonstrate accurate identification of candidates that will have quickly left the organization. The system has been deployed as described in a large globally integrated enterprise.", + "source_url": "https://doi.org/10.1145/2452376.2452453", + "assessment": "Directly presents algorithmic candidate ranking and decision support with hiring-yield evidence, but predates the window and partly optimizes post-hire retention.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W4381848566", + "type": "candidate", + "title": "Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation", + "subtitle": "pending review \u00b7 2023", + "body": "Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system\u2019s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system\u2019s life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight; robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental wellbeing; and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society.", + "source_url": "https://doi.org/10.1016/j.inffus.2023.101896", + "assessment": "Provides general AI oversight, fairness, privacy, transparency, accountability, and auditing concepts, but no hiring-specific application.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W4400303336", + "type": "candidate", + "title": "Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making", + "subtitle": "pending review \u00b7 2024", + "body": "The rapid integration of artificial intelligence (AI) systems into various domains has raised concerns about their impact on individual and societal wellbeing, particularly due to the lack of transparency and accountability in their decision-making processes. This review aims to provide an overview of the key legal and ethical challenges associated with implementing transparency and accountability in AI systems. The review identifies four main thematic areas: technical approaches, legal and regulatory frameworks, ethical and societal considerations, and interdisciplinary and multi-stakeholder approaches. By synthesizing the current state of research and proposing key strategies for policymakers, this review contributes to the ongoing discourse on responsible AI governance and lays the foundation for future research in this critical area. Ultimately, the goal is to promote individual and societal wellbeing by ensuring that AI systems are developed and deployed in a transparent, accountable, and ethical manner.", + "source_url": "https://doi.org/10.3389/fhumd.2024.1421273", + "assessment": "Reviews general AI transparency and accountability rather than employment or hiring-specific systems.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W2994948545", + "type": "candidate", + "title": "Considerations for AI fairness for people with disabilities", + "subtitle": "pending review \u00b7 2019", + "body": "In society today, people experiencing disability can face discrimination. As artificial intelligence solutions take on increasingly important roles in decision-making and interaction, they have the potential to impact fair treatment of people with disabilities in society both positively and negatively. We describe some of the opportunities and risks across four emerging AI application areas: employment, education, public safety, and healthcare, identified in a workshop with participants experiencing a range of disabilities. In many existing situations, non-AI solutions are already discriminatory, and introducing AI runs the risk of simply perpetuating and replicating these flaws. We next discuss strategies for supporting fairness in the context of disability throughout the AI development lifecycle. AI systems should be reviewed for potential impact on the user in their broader context of use. They should offer opportunities to redress errors, and for users and those impacted to raise fairness concerns. People with disabilities should be included when sourcing data to build models, and in testing, to create a more inclusive and robust system. Finally, we offer pointers into an established body of literature on human-centered design processes and philosophies that may assist AI and ML engineers in innovating algorithms that reduce harm and ultimately enhance the lives of people with disabilities.", + "source_url": "https://doi.org/10.1145/3362077.3362086", + "assessment": "Includes employment among several disability-fairness domains and discusses redress and inclusive testing, but is broad and outside the publication window.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W1976888500", + "type": "candidate", + "title": "Hiring as Cultural Matching", + "subtitle": "pending review \u00b7 2012", + "body": "This article presents culture as a vehicle of labor market sorting. Providing a case study of hiring in elite professional service firms, I investigate the often suggested but heretofore empirically unexamined hypothesis that cultural similarities between employers and job candidates matter for employers\u2019 hiring decisions. Drawing from 120 interviews with employers as well as participant observation of a hiring committee, I argue that hiring is more than just a process of skills sorting; it is also a process of cultural matching between candidates, evaluators, and firms. Employers sought candidates who were not only competent but also culturally similar to themselves in terms of leisure pursuits, experiences, and self-presentation styles. Concerns about shared culture were highly salient to employers and often outweighed concerns about absolute productivity. I unpack the interpersonal processes through which cultural similarities affected candidate evaluation in elite firms and provide the first empirical demonstration that shared culture\u2014particularly in the form of lifestyle markers\u2014matters for employer hiring. I conclude by discussing the implications for scholarship on culture, inequality, and labor markets.", + "source_url": "https://doi.org/10.1177/0003122412463213", + "assessment": "Examines human cultural matching in hiring, but contains no AI and predates the publication window.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W1542246681", + "type": "candidate", + "title": "Recruiting Through the Stages: A Meta\u2010Analytic Test of Predictors of Applicant Attraction at Different Stages of the Recruiting Process", + "subtitle": "pending review \u00b7 2012", + "body": "We used meta\u2010analysis and semipartial correlations to examine the relative strength and incremental variance accounted for by 7 categories of recruiting predictors across multiple recruitment stages on applicant attraction. Based on 232 studies (250 samples, 3,518 coefficients, n = 108,632), we found that characteristics of the job, organization, and recruitment process, recruiter behaviors, perceived fit, and hiring expectancies (but not perceived alternatives) accounted for unique variance in applicant attraction at multiple stages. Perceived fit was the strongest relative and unique variance predictor of applicant attraction albeit a nonsignificant predictor of job choice. Although not among the largest zero\u2010order predictors, recruiter behaviors accounted for substantial incremental variance at the first 2 stages. Organizational characteristics are more heavily weighed by applicants when maintaining applicant status as compared to the stage of application, and recruitment process characteristics are weighed progressively more as the recruitment stages advance. Job characteristics accounted for the greatest unique variance in job choice decisions. Job characteristics are more predictive in field studies, whereas recruiter behaviors, recruitment process characteristics, hiring expectancies, and perceived alternatives produced larger effect sizes in the laboratory. Results are discussed in terms of their theoretical and practical implications with future research suggestions.", + "source_url": "https://doi.org/10.1111/j.1744-6570.2012.01254.x", + "assessment": "Relevant to applicant attraction across recruitment stages, but contains no AI and predates the publication window.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W2507358938", + "type": "candidate", + "title": "Algorithmic Bias", + "subtitle": "pending review \u00b7 2016", + "body": "Algorithms and decision making based on Big Data have become pervasive in all aspects of our daily lives lives (offline and online), as they have become essential tools in personal finance, health care, hiring, housing, education, and policies. It is therefore of societal and ethical importance to ask whether these algorithms can be discriminative on grounds such as gender, ethnicity, or health status. It turns out that the answer is positive: for instance, recent studies in the context of online advertising show that ads for high-income jobs are presented to men much more often than to women [Datta et al., 2015]; and ads for arrest records are significantly more likely to show up on searches for distinctively black names [Sweeney, 2013]. This algorithmic bias exists even when there is no discrimination intention in the developer of the algorithm. Sometimes it may be inherent to the data sources used (software making decisions based on data can reflect, or even amplify, the results of historical discrimination), but even when the sensitive attributes have been suppressed from the input, a well trained machine learning algorithm may still discriminate on the basis of such sensitive attributes because of correlations existing in the data. These considerations call for the development of data mining systems which are discrimination-conscious by-design. This is a novel and challenging research area for the data mining community.", + "source_url": "https://doi.org/10.1145/2939672.2945386", + "assessment": "Mentions hiring as one example of algorithmic discrimination and proxy effects, but is broad and outside the publication window.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W104159351", + "type": "candidate", + "title": "Making the most of on-line recruiting.", + "subtitle": "pending review \u00b7 2001", + "body": "Ninety percent of large U.S. companies are already recruiting via the Internet. By simply logging on to the Web, company recruiters can locate vast numbers of qualified candidates for jobs at every level, screen them in minutes, and contact the most promising ones immediately. The payoffs can be enormous: it costs substantially less to hire someone on-line, and the time saved is equally great. In this article, Peter Cappelli examines some of the emerging service providers and technologies--matchmakers, job boards, hiring management systems software, and applicant-screening mechanisms that test skills and record interests. He also looks at some of the strategies companies are adopting as they enter on-line labor markets. Recruiting needs to be refashioned to resemble marketing, he stresses. Accordingly, smart companies are designing Web pages, and even product ads, with potential recruits in mind. They're giving line managers authority to hire so that candidates in cyberspace aren't lost. They're building internal on-line job networks to retain talent. Integrating recruiting efforts with overall marketing campaigns, especially through coordination and identification with the company's brand, is the most important thing companies can do to ensure success in on-line hiring. Along the way, Cappelli sounds two cautionary notes. First, a human touch, not electronic contact, is vital in the last steps of a successful hiring process. Second, companies must make sure that on-line testing and hiring criteria do not discriminate against women, disabled people, workers over 40, or members of minority groups. When competition for talent is fierce, companies that master the art and science of on-line recruiting will be the ones that attract and keep the best people.", + "source_url": "https://pubmed.ncbi.nlm.nih.gov/11246921", + "assessment": "Discusses online recruiting efficiency, screening, human involvement, and discrimination, but not modern AI and far predates the window.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W1491998907", + "type": "candidate", + "title": "E-recruitment: Towards an Ubiquitous Recruitment Process and Candidate Relationship Management", + "subtitle": "pending review \u00b7 2012", + "body": "Up to now, there has been little research on the impact of e-recruitment on the recruitment process as a whole. The present study fills part of this gap by investigating the effect of e-recruitment on the design of the recruitment process. Three explorative case studies were carried out in three large organisations in Denmark in 2008\u20132010. The findings indicate that e-recruitment transforms the traditional recruitment process into a time- and space-independent, collaborative hiring process. The most significant changes are recorded in the sequence and increased divisibility of main recruitment tasks and subtasks. For management, the main task is now that of communicating with candidates. In addition, a new on-going task of maintaining a corporate career website has become an integral part of the new recruitment process. The new design is presented in the following, and its implications briefly discussed.", + "source_url": "https://doi.org/10.1177/239700221202600303", + "assessment": "Examines how e-recruitment changes hiring workflow and candidate communication, but does not concern AI and predates the window.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W4391652653", + "type": "candidate", + "title": "Methodological Approach to Assessing the Current State of Organizations for AI-Based Digital Transformation", + "subtitle": "pending review \u00b7 2024", + "body": "In an era defined by technological disruption, the integration of artificial intelligence (AI) into business processes is both strategic and challenging. As AI continues to disrupt and reshape industries and revolutionize business processes, organizations must take proactive steps to assess their readiness and capabilities to effectively leverage AI technologies. This research focuses on the assessment elements required to evaluate an organization\u2019s current state in preparation for AI-based digital transformation. This research is based on a literature review and practical insights derived from extensive experience in industrial system engineering. This paper outlines the key assessment elements that organizations should consider to ensure successful and sustainable AI-based digital transformation. This emphasizes the need for a comprehensive approach to assess the organization\u2019s data infrastructure, governance practices, and existing AI capabilities. Furthermore, the research work focuses on the evaluation of AI talent and skills within the organization, considering the significance of fostering an innovative culture and addressing change management challenges. The results of this study provide organizations with elements to assess their current state for AI-based digital transformation. By adopting and implementing the proposed guidelines, organizations can gain a holistic perspective of their current standing, identify strategic opportunities for AI integration, mitigate potential risks, and strategize a successful path forwards in the evolving landscape of AI-driven digital transformation.", + "source_url": "https://doi.org/10.3390/asi7010014", + "assessment": "Discusses organizational AI readiness, data infrastructure, and governance generally, without application to hiring.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W2512694330", + "type": "candidate", + "title": "Talent development gamification in talent selection assessment centres", + "subtitle": "pending review \u00b7 2016", + "body": "Purpose The purpose of this paper is to examine the relationship between the use of sophisticated talent selection processes such as gamification and training and development interventions designed to ensure that candidates can successfully navigate the talent assessment process. Gamification is the application of game elements to non-game activities through the adoption of gaming tools, and little is known about how candidates (\u201ctalent\u201d) struggle to learn about the structural mechanics of gamification as they engage with the hidden rules of talent selection, such as goals, rules, \u201clevelling up\u201d, feedback and engagement in competitive \u2013 collaborative activities. The term \u201ctalent development gamification\u201d is coined and used as an analytical tool to consider how young talent are supported by development interventions in their inter-subjectivity as they learn how to survive and win in talent selection games. Design/methodology/approach Studying hidden dynamics in development processes inherent in gamified talent selection is challenging, so a cult work of fiction, \u201cEnder\u2019s Game\u201d, is examined to address the questions: \u201cHow do candidates in talent selection programmes learn to make sense of the structural mechanics of gamification\u201d, \u201cHow does this make the hidden rules of talent selection explicit to them?\u201d and \u201cWhat does this mean for talent development?\u201d Findings Talent development in selection gamification processes is illustrated through nuanced theoretical accounts of how a multiplicity of shifting and competing developmental learning opportunities are played out as a form of \u201cdouble-consciousness\u201d by potential organizational talent for them to \u201cwin the selection game\u201d. Research limitations/implications Using novels as an aid to understanding management and the organization of work is ontologically and epistemologically problematic. But analysing novels which are \u201cgood reads\u201d also has educational value and can produce new knowledge from its analysis. In exploring how \u201cCharacters are made to live dangerously, to face predicaments that, as readers, we experience as vicarious pleasure. We imagine, for example, how a particular character may react or, more importantly, what we would do in similar circumstances\u201d (Knights and Willmott, 1999, p. 5). This future-oriented fictional narrative is both illustrative and provides an analogy to illuminate current organisational development challenges. Originality/value The term \u201ctalent development gamification in selection processes\u201d is coined to allow analysis and provide lessons for talent development practice in a little studied area. Our case study analysis identifies a number of areas for consideration by talent management/talent development specialists involved in developing talent assessment centres incorporating gamification. These include the importance of understanding and taking account of rites of passage through the assessment centre, in particular the role of liminal space, what talent development interventions might be of benefit and the necessity of appreciating and managing talent in developing the skill of double consciousness in game simulations.", + "source_url": "https://doi.org/10.1108/ejtd-03-2016-0017", + "assessment": "Concerns gamified talent assessment and candidate gaming, but not AI and outside the publication window.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W1901676042", + "type": "candidate", + "title": "Simply the best? The case for using \u2018threshold selection\u2019 in hiring decisions", + "subtitle": "pending review \u00b7 2011", + "body": "This paper argues the case for a more progressive approach to diversity and inclusion that incorporates a form of positive discrimination in the selection process: \u2018threshold selection\u2019. It outlines the distinctive features of threshold selection and argues that it is an approach that resolves the tension between suitability and acceptability criteria, relies on high formalisation and transparency, can mitigate some of the self\u2010serving bias among selectors and ties the selection needs to the goals of the organisation. In these respects, although controversial, threshold selection offers a thorough and systematic approach that is entirely consistent with the general tendency towards greater professionalisation of HRM.", + "source_url": "https://doi.org/10.1111/j.1748-8583.2011.00168.x", + "assessment": "Discusses transparent threshold-based human selection and diversity, but contains no AI and predates the window.", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W2067108088", + "type": "candidate", + "title": "Perceiver self-presentational goals as moderators of expectancy influences: Ingratiation and the disconfirmation of negative expectancies.", + "subtitle": "pending review \u00b7 1993", + "body": "Self-fulfilling prophecy processes enable people to confirm their negative expectancies for others. The perceiver goal of ingratiation was hypothesized to alter this behavioral dynamic and thus lead perceivers to disconfirm their negative expectancies. In an interview setting, we manipulated interviewer Ss' expectancies and interaction goals. As anticipated, \"no goal\" interviewers were relatively cold and challenging toward their negative-expectancy applicants; as a result, these applicants performed somewhat less favorably, consistent with interviewer expectancies. In contrast, \"liking goal\" interviewers were relatively warm and unthreatening toward their negative-expectancy applicants; as a result, these applicants performed favorably, disconfirming interviewer expectancies. These data support a framework in which perceiver self-presentation goals are conceptualized to moderate the expectancy-confirmation process.", + "source_url": "https://doi.org/10.1037//0022-3514.64.3.409", + "assessment": "include=none; topic-token-hits=1; exclude=none", + "links": [ + "candidate-queue" + ] + }, + { + "id": "candidate:openalex:W3203196106", + "type": "candidate", + "title": "Screened Out Onscreen: Disability Discrimination, Hiring Bias, and Artificial Intelligence", + "subtitle": "pending review \u00b7 2021", + "body": "Abstract unavailable.", + "source_url": "https://doi.org/10.2139/ssrn.3906300", + "assessment": "include=none; topic-token-hits=1; exclude=none", + "links": [ + "candidate-queue" + ] + } + ], + "page_count": 42 + }, + "opportunities": { + "generated_at": "2026-06-20T10:46:15.552006+00:00", + "analysis_model": "codex-cli:configured-model", + "summary": "The accepted corpus supports bounded opportunities in human\u2013AI decision workflows, predictive validity, operational value, accessibility, and governance. These are corpus gaps, evidence gaps, or translation gaps\u2014not claims of field-wide absence. One accepted record (\u201cFormal proof search\u201d) appears unrelated to hiring and weakens corpus reliability.", + "opportunities": [ + { + "rank": 1, + "title": "Human\u2013AI decision workflows, overrides, and accountability", + "gap_type": "evidence_gap", + "priority_score": 95, + "confidence": "high", + "scope": "Empirical studies of recruiter or hiring-manager interaction with AI recommendations, including reliance, overrides, escalation, accountability, and resulting hiring outcomes.", + "evidence": [ + { + "observation": "The corpus shape reports zero coverage for decision roles and risk mechanisms.", + "source": "Report excerpt: Corpus Shape" + }, + { + "observation": "The algorithmic inclusion paper explicitly states that evidence is lacking on human\u2013machine interaction in hiring decisions and effective auditing.", + "source": "openalex:W4366989571" + }, + { + "observation": "The Thailand study measures intention to adopt AI but does not report observed decision behavior or hiring outcomes.", + "source": "openalex:W4390880618" + } + ], + "llm_reasoning": "The corpus discusses collaboration, adoption, and oversight conceptually, but provides no accepted empirical study of how recruiters use, reject, or defer to AI recommendations. This is a bounded evidence gap within the accepted corpus.", + "uncertainty": "Relevant empirical studies may exist among rejected candidates or outside the current search/index coverage. The available abstracts may also omit workflow details present in full texts.", + "coverage_check": { + "query_terms": [ + "human AI hiring override behavior", + "recruiter reliance algorithmic recommendations", + "automation bias recruitment field experiment", + "hiring escalation accountability AI" + ], + "matched_titles": [ + "Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring", + "A UTAUT-Based Framework for Analyzing Users\u2019 Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand", + "Ethics and discrimination in artificial intelligence-enabled recruitment practices" + ], + "interpretation": "Matched papers motivate human oversight or adoption, but the supplied evidence does not establish observed override behavior, automation bias, or accountability outcomes." + }, + "research_questions": [ + "How often do recruiters override AI recommendations, and under what conditions?", + "How do explanations, confidence scores, and review requirements affect reliance and decision quality?", + "Who is accountable when human and algorithmic recommendations conflict?", + "Do human-review controls reduce adverse impact without eliminating efficiency gains?" + ], + "scout_queries": [ + "(\"algorithmic hiring\" OR \"AI recruitment\") recruiter override reliance experiment", + "(\"automation bias\" OR algorithm aversion) hiring recruitment selection", + "human-in-the-loop hiring decision support field study accountability", + "AI hiring recommendation disagreement escalation recruiter" + ] + }, + { + "rank": 2, + "title": "Predictive validity and generalization across jobs and populations", + "gap_type": "evidence_gap", + "priority_score": 92, + "confidence": "high", + "scope": "Independent, job-related validation of AI screening and assessment systems across occupations, applicant populations, locations, and time periods.", + "evidence": [ + { + "observation": "The chatbot study reports validity for some personality traits but not others, and reports no significant prediction of real-world outcomes.", + "source": "openalex:W4409825334" + }, + { + "observation": "The chatbot sample contains 159 candidates and professionals from Serbian and Montenegrin regions.", + "source": "openalex:W4409825334" + }, + { + "observation": "The remaining accepted papers primarily address fairness, adoption, ethics, or inclusion rather than criterion-related prediction of job performance or hiring success.", + "source": "Accepted-paper corpus" + } + ], + "llm_reasoning": "The only accepted paper supplying direct predictive-validity evidence reports weak external validity in a geographically bounded sample. The corpus therefore cannot support general claims about quality-of-hire or generalization.", + "uncertainty": "Full texts of other accepted papers may contain limited validation evidence not visible in the abstracts. Relevant validation studies may also use product-specific terminology absent from current queries.", + "coverage_check": { + "query_terms": [ + "predictive validity AI hiring", + "criterion validity algorithmic assessment", + "quality of hire AI longitudinal", + "generalization across jobs populations" + ], + "matched_titles": [ + "Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity" + ], + "interpretation": "Only one supplied match directly evaluates predictive validity, and its results are mixed and context-specific." + }, + "research_questions": [ + "Which AI-derived hiring scores predict job performance, retention, or training outcomes beyond established assessments?", + "How stable are validity coefficients across occupations, languages, applicant groups, and labor markets?", + "How do model updates and distribution shifts affect criterion validity over time?", + "What sample sizes and validation designs are required for credible deployment decisions?" + ], + "scout_queries": [ + "(\"AI hiring\" OR \"algorithmic selection\") \"criterion-related validity\"", + "(\"machine learning\" recruitment assessment) predictive validity job performance", + "AI hiring longitudinal \"quality of hire\" validation", + "algorithmic assessment cross-validation occupation population generalization" + ] + }, + { + "rank": 3, + "title": "Measured operational value and quality trade-offs", + "gap_type": "evidence_gap", + "priority_score": 89, + "confidence": "high", + "scope": "Field experiments or longitudinal evaluations measuring time-to-hire, cost-per-hire, recruiter workload, conversion, quality-of-hire, and fairness jointly.", + "evidence": [ + { + "observation": "The topic contract identifies time-to-hire, cost-per-hire, recruiter workload, conversion, and quality-of-hire as dashboard metrics.", + "source": "Topic contract" + }, + { + "observation": "The ethics paper states that AI may increase efficiency and reduce transactional work, but the supplied abstract does not provide quantified operational outcomes.", + "source": "openalex:W4386714740" + }, + { + "observation": "The Thailand paper studies perceived value and adoption intention rather than realized business metrics.", + "source": "openalex:W4390880618" + }, + { + "observation": "The corpus shape reports only one paper under evaluation dimensions.", + "source": "Report excerpt: Corpus Shape" + } + ], + "llm_reasoning": "Operational benefits are asserted or perceived in the accepted corpus, but the supplied evidence does not quantify realized value or connect efficiency gains to validity, fairness, and candidate outcomes.", + "uncertainty": "The abstracts may omit numerical results available in full papers. Vendor or internal organizational studies may exist but may not meet the contract\u2019s evidence and independence requirements.", + "coverage_check": { + "query_terms": [ + "time to hire AI recruitment", + "cost per hire AI", + "recruiter workload field experiment", + "quality of hire longitudinal" + ], + "matched_titles": [ + "Ethics and discrimination in artificial intelligence-enabled recruitment practices", + "A UTAUT-Based Framework for Analyzing Users\u2019 Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand" + ], + "interpretation": "The supplied matches discuss efficiency or perceived value, not controlled measurements of realized business value and downstream quality." + }, + "research_questions": [ + "What causal effect does AI assistance have on time-to-hire and recruiter workload?", + "Do efficiency gains persist after including review, audit, appeal, and monitoring costs?", + "How do operational gains trade off against predictive validity, fairness, and candidate experience?", + "Which hiring stages produce measurable value under assistive versus semi-automated deployment?" + ], + "scout_queries": [ + "AI recruitment field experiment time-to-hire recruiter workload", + "algorithmic hiring cost-per-hire causal evaluation", + "AI screening longitudinal quality-of-hire conversion", + "recruitment automation ROI independent evaluation fairness" + ] + }, + { + "rank": 4, + "title": "Accessibility mechanisms and stage-specific outcomes for disabled applicants", + "gap_type": "evidence_gap", + "priority_score": 84, + "confidence": "high", + "scope": "Stage-level studies of accessibility barriers, accommodations, disability-specific measurement validity, progression, final selection, and appeal mechanisms.", + "evidence": [ + { + "observation": "The UAE study reports different sentiment across disability types and lower final hiring outcomes for disabled applicants despite no significant difference in initial progression.", + "source": "openalex:W4404228668" + }, + { + "observation": "The supplied corpus contains no second disability-focused accepted paper for replication or comparison.", + "source": "Accepted-paper corpus" + }, + { + "observation": "The fairness review emphasizes vulnerable groups but the supplied abstract does not provide disability-specific stage-level outcome evidence.", + "source": "openalex:W4394590365" + } + ], + "llm_reasoning": "The UAE study identifies a stage-dependent disparity but does not, from the supplied abstract, isolate whether accessibility, measurement validity, workflow design, or human selection decisions caused it. Replication and mechanism-focused evidence are needed before interpreting the result.", + "uncertainty": "The full UAE paper may contain additional controls or tool descriptions. Results may not generalize beyond the UAE, the examined tools, or represented disability categories.", + "coverage_check": { + "query_terms": [ + "disabled applicants AI hiring accessibility", + "reasonable accommodation algorithmic assessment", + "disability adverse impact recruitment AI", + "accessible automated interview" + ], + "matched_titles": [ + "Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE)", + "Fairness, AI & recruitment" + ], + "interpretation": "One empirical paper provides disability-specific outcomes; the supplied corpus lacks comparative evidence across tools, jurisdictions, stages, and accommodation designs." + }, + "research_questions": [ + "At which hiring stage do disability-related outcome differences emerge, and through which mechanism?", + "Are AI assessment constructs and interfaces valid across disability types and assistive technologies?", + "How do accommodation, disclosure, human review, and appeal procedures alter outcomes?", + "Do accessibility improvements affect predictive validity or false-negative rates?" + ], + "scout_queries": [ + "(\"AI hiring\" OR \"automated employment decision\") disability accessibility outcomes", + "algorithmic recruitment reasonable accommodation assistive technology", + "automated interview disability validation adverse impact", + "AI employment assessment disabled applicants appeal human review" + ] + }, + { + "rank": 5, + "title": "Operationalizing fairness into auditable controls", + "gap_type": "translation_gap", + "priority_score": 82, + "confidence": "high", + "scope": "Implementation and evaluation of fairness definitions through documentation, subgroup testing, audit procedures, monitoring thresholds, remediation, appeals, and vendor assurance.", + "evidence": [ + { + "observation": "The fairness review states that fairness has different meanings across stakeholders and focuses on definition, categorization, and practical implementation.", + "source": "openalex:W4394590365" + }, + { + "observation": "The bias-mitigation review identifies vector-space correction and data augmentation as mitigation techniques.", + "source": "openalex:W4391598857" + }, + { + "observation": "The ethics paper recommends dataset, transparency, internal governance, and external oversight measures.", + "source": "openalex:W4386714740" + }, + { + "observation": "The corpus shape reports only one governance-control paper, whose supplied evidence concerns adoption intention rather than control effectiveness.", + "source": "Report excerpt and openalex:W4390880618" + } + ], + "llm_reasoning": "The corpus proposes fairness concepts and mitigation methods, but the supplied evidence does not show how organizations select fairness criteria, establish audit thresholds, respond to failures, or verify that controls improve outcomes in deployment.", + "uncertainty": "Detailed implementation guidance may appear in the full review articles. Jurisdiction-specific standards and audits may be indexed under legal or technical terminology not captured by the accepted corpus.", + "coverage_check": { + "query_terms": [ + "algorithmic hiring fairness audit implementation", + "adverse impact monitoring AI recruitment", + "vendor assurance automated hiring", + "appeal contestability hiring algorithm" + ], + "matched_titles": [ + "Fairness, AI & recruitment", + "A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring", + "Ethics and discrimination in artificial intelligence-enabled recruitment practices", + "Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring" + ], + "interpretation": "The supplied papers identify fairness and governance needs, but do not provide accepted evidence of deployed control effectiveness or repeatable audit workflows." + }, + "research_questions": [ + "How should employers choose fairness metrics for specific hiring stages and legal contexts?", + "Which audit and monitoring controls detect meaningful adverse impact after deployment?", + "How effective are remediation methods under intersectional and distribution-shift conditions?", + "What evidence should vendors provide to employers, regulators, applicants, and auditors?" + ], + "scout_queries": [ + "algorithmic hiring audit protocol adverse impact monitoring", + "AI recruitment fairness metrics implementation case study", + "automated employment decision tool vendor audit assurance", + "algorithmic hiring appeal contestability remediation monitoring" + ] + }, + { + "rank": 6, + "title": "Corpus integrity and false-positive acceptance control", + "gap_type": "corpus_gap", + "priority_score": 98, + "confidence": "high", + "scope": "Validate accepted-paper relevance, identifiers, URLs, and provenance before using corpus counts or gap conclusions.", + "evidence": [ + { + "observation": "\u201cFormal proof search\u201d has the abstract \u201cProof search with verifier feedback,\u201d topic \u201cproof search,\u201d and no stated connection to hiring.", + "source": "openalex:1" + }, + { + "observation": "Its source is listed as https://example.com rather than a scholarly or stable source URL.", + "source": "Report excerpt: Papers" + }, + { + "observation": "The record was accepted from a scout run using only the query \u201cq.\u201d", + "source": "Scout run dated 2026-06-20" + }, + { + "observation": "The research contract requires every paper to retain a source URL and stable identifier and requires rejection of adjacent out-of-scope work.", + "source": "AGENTS.md research contract and hard rules" + } + ], + "llm_reasoning": "This record is inconsistent with the topic contract and appears to be a false positive or test fixture. Because the corpus has only eight accepted records, retaining it materially distorts coverage counts and downstream gap analysis.", + "uncertainty": "The supplied record could represent intentionally injected test data. Its relevance cannot be recovered from the provided metadata.", + "coverage_check": { + "query_terms": [ + "formal proof search hiring recruitment employment selection", + "openalex:1 provenance" + ], + "matched_titles": [ + "Formal proof search" + ], + "interpretation": "The record matches no hiring concept in its title, abstract, or topic metadata and lacks a credible source URL." + }, + "research_questions": [ + "Is openalex:1 a test fixture or an accidental acceptance?", + "Does its identifier resolve to a real scholarly record?", + "Which validation rule allowed a non-hiring paper and placeholder URL to pass acceptance?", + "Should corpus statistics be regenerated after relevance and provenance validation?" + ], + "scout_queries": [ + "validate openalex:1 title abstract source identifier", + "\"Formal proof search\" hiring recruitment", + "accepted corpus placeholder URL example.com audit", + "false positive relevance filter AI hiring corpus" + ] + } + ] + }, + "papers": [ + { + "id": "openalex:1", + "title": "Formal proof search", + "year": 2025, + "url": "https://example.com", + "doi": null, + "abstract": "Proof search with verifier feedback.", + "citation_count": 10, + "topics": [ + "proof search" + ], + "discovered_via": [ + "query:q" + ], + "relevance_score": 8.0, + "relevance_reason": "aligned", + "accepted_at": "2026-06-20T09:31:25.347504+00:00", + "primary_category": "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication", + "categories": [], + "notes": "" + }, + { + "id": "openalex:W4409825334", + "title": "Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity", + "year": 2025, + "url": "https://doi.org/10.3389/fpsyg.2025.1564979", + "doi": "https://doi.org/10.3389/fpsyg.2025.1564979", + "abstract": "This paper explores the efficacy of AI-driven chatbots in accurately inferring personality traits compared to traditional psychometric tests within a real-world professional hiring context. The study is driven by the increasing integration of AI tools in recruitment processes, which necessitates a deeper understanding of their reliability and validity. Using a quasi-experimental design with propensity score matching, we analysed data from 159 candidates and other professionals from Serbian and Montenegrin regions who completed both traditional psychometric assessments and AI-based personality evaluations based on the Big Five Personality model. A novel one-question-per-facet approach was employed in the chatbot assessments with a goal of enabling more granular analysis of the chatbot's psychometric properties. The findings indicate that the chatbot demonstrated good structural, substantive, and convergent validity for certain traits, particularly Extraversion and Conscientiousness, but not for Neuroticism, Agreeableness, and Openness. While robust regression confirmed that AI-inferred scores are less susceptible to social desirability bias than traditional tests, they did not significantly predict real-world outcomes, indicating issues with external validity, particularly predictive validity. The results suggest that AI-driven chatbots show promise for identifying certain personality traits and demonstrate resistance to social desirability bias. This paper contributes to the emerging field of AI and psychometrics by offering insights into the potential and limitations of AI tools in professional selection, while developing an approach for refining psychometric properties of AI-driven assessments.", + "citation_count": 2, + "topics": [ + "Personality Traits and Psychology", + "Employer Branding and e-HRM", + "AI in Service Interactions" + ], + "discovered_via": [ + "query:AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability" + ], + "relevance_score": 10.0, + "relevance_reason": "Direct hiring-context comparison of AI chatbot assessments and psychometric tests, with concrete evidence on validity, reliability, predictive performance, and social-desirability bias.", + "heuristic_relevance_score": 1.2, + "heuristic_relevance_reason": "include=none; topic-token-hits=2; exclude=none", + "accepted_at": "2026-06-20T09:55:51.437095+00:00", + "primary_category": "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication", + "categories": [], + "notes": "" + }, + { + "id": "openalex:W4404228668", + "title": "Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE)", + "year": 2024, + "url": "https://doi.org/10.56294/dm2025487", + "doi": "https://doi.org/10.56294/dm2025487", + "abstract": "IntroductionThis research aims to explore the effectiveness and inclusivity of AI-powered recruitment tools in hiring people with disabilities within the United Arab Emirates. Such is the situation where AI integration into the arena of recruitment is increasingly rapid, while there are vital issues on the side of bias, accessibility, and fairness for applicants of diverse needs. MethodsThis study was a mixed-methods approach, examining sentiment analysis, emotion detection, and HR analytics of feedback from applicants with a disability, 415 in total. The research focused on scores referring to sentiment, the progression rate, and the outcome of the final hiring. ResultsThe sentiment score varied significantly across disability types (p-value <0.05). The applicants with cognitive disability expressed the highest sentiment sore while applicants with hearing impairment had the lowest, which indicated the varying adaptability of AI. The emotion analysis depicted a mix of positive and negative emotions. A few applicants liked technology and have trust in it, while others report fear. Clearly, the applicants, both disabled and non-disabled did not differ in their rate of progression (p-value >0.05), hence never indicating any significant difference within the initial steps of the process. The final hiring stage showed significant differences in results with (p-value <0.05), where the proportionate number of disabled applicants was recorded to be lower than that of non-disabled applicants", + "citation_count": 110, + "topics": [ + "Organizational and Employee Performance", + "AI in Service Interactions", + "Cyberloafing and Workplace Behavior" + ], + "discovered_via": [ + "query:algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability" + ], + "relevance_score": 9.0, + "relevance_reason": "Direct mixed-methods evaluation of accessibility, fairness, applicant experience, progression, and hiring outcomes for disabled applicants using AI recruitment tools in the UAE.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "accepted_at": "2026-06-20T09:55:51.437095+00:00", + "primary_category": "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication", + "categories": [], + "notes": "" + }, + { + "id": "openalex:W4394590365", + "title": "Fairness, AI & recruitment", + "year": 2024, + "url": "https://doi.org/10.1016/j.clsr.2024.105966", + "doi": "https://doi.org/10.1016/j.clsr.2024.105966", + "abstract": "The ever-increasing adoption of AI technologies in the hiring landscape to enhance human resources efficiency raises questions about algorithmic decision-making's implications in employment, especially for job applicants, including those at higher risk of social discrimination. Among other concepts, such as transparency and accountability, fairness has become crucial in AI recruitment debates due to the potential reproduction of bias and discrimination that can disproportionately affect certain vulnerable groups. However, the ideals and ambitions of fairness may signify different meanings to various stakeholders. Conceptualizing fairness is critical because it may provide a clear benchmark for evaluating and mitigating biases, ensuring that AI systems do not perpetuate existing imbalances and promote, in this case, equitable opportunities for all candidates in the job market. To this end, in this article, we conduct a scoping literature review on fairness in AI applications for recruitment and selection purposes, with special emphasis on its definition, categorization, and practical implementation. We start by explaining how AI applications have been increasingly used in the hiring process, especially to increase the efficiency of the HR team. We then move to the limitations of this technological innovation, which is known to be at high risk of privacy violations and social discrimination. Against this backdrop, we focus on defining and operationalizing fairness in AI applications for recruitment and selection purposes through cross-disciplinary lenses. Although the applicable legal frameworks and some research currently address the issue piecemeal, we observe and welcome the emergence of some cross-disciplinary efforts aimed at tackling this multifaceted challenge. We conclude the article with some brief recommendations to guide and shape future research and action on the fairness of AI applications in the hiring process for the better.", + "citation_count": 77, + "topics": [ + "Employer Branding and e-HRM", + "Ethics and Social Impacts of AI", + "Names, Identity, and Discrimination Research" + ], + "discovered_via": [ + "query:algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability" + ], + "relevance_score": 9.0, + "relevance_reason": "Direct scoping review of fairness definitions and implementation in AI recruitment and selection, including bias, vulnerable groups, privacy, transparency, accountability, and legal frameworks.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "accepted_at": "2026-06-20T09:55:51.437095+00:00", + "primary_category": "Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security", + "categories": [], + "notes": "" + }, + { + "id": "openalex:W4386714740", + "title": "Ethics and discrimination in artificial intelligence-enabled recruitment practices", + "year": 2023, + "url": "https://doi.org/10.1057/s41599-023-02079-x", + "doi": "https://doi.org/10.1057/s41599-023-02079-x", + "abstract": "Abstract This study aims to address the research gap on algorithmic discrimination caused by AI-enabled recruitment and explore technical and managerial solutions. The primary research approach used is a literature review. The findings suggest that AI-enabled recruitment has the potential to enhance recruitment quality, increase efficiency, and reduce transactional work. However, algorithmic bias results in discriminatory hiring practices based on gender, race, color, and personality traits. The study indicates that algorithmic bias stems from limited raw data sets and biased algorithm designers. To mitigate this issue, it is recommended to implement technical measures, such as unbiased dataset frameworks and improved algorithmic transparency, as well as management measures like internal corporate ethical governance and external oversight. Employing Grounded Theory, the study conducted survey analysis to collect firsthand data on respondents\u2019 experiences and perceptions of AI-driven recruitment applications and discrimination.", + "citation_count": 351, + "topics": [ + "Ethics and Social Impacts of AI", + "Employer Branding and e-HRM", + "AI and HR Technologies" + ], + "discovered_via": [ + "query:artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection" + ], + "relevance_score": 8.0, + "relevance_reason": "Directly addresses AI recruitment efficiency, discriminatory outcomes, biased data and design, transparency, governance, and external oversight using review and survey evidence.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "accepted_at": "2026-06-20T09:55:51.437095+00:00", + "primary_category": "Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor", + "categories": [], + "notes": "" + }, + { + "id": "openalex:W4391598857", + "title": "A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring", + "year": 2024, + "url": "https://doi.org/10.3390/ai5010019", + "doi": "https://doi.org/10.3390/ai5010019", + "abstract": "The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms\u2019 fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society.", + "citation_count": 112, + "topics": [ + "AI and HR Technologies", + "Artificial Intelligence in Healthcare and Education", + "AI in Service Interactions" + ], + "discovered_via": [ + "query:algorithmic hiring employment discrimination automated decision systems law regulation audit privacy" + ], + "relevance_score": 8.0, + "relevance_reason": "Direct systematic review of bias and mitigation in AI-based CV screening, including efficiency, fairness techniques, case studies, and human\u2013machine collaboration.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "accepted_at": "2026-06-20T09:55:51.437095+00:00", + "primary_category": "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication", + "categories": [], + "notes": "" + }, + { + "id": "openalex:W4366989571", + "title": "Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring", + "year": 2023, + "url": "https://doi.org/10.1111/1748-8583.12511", + "doi": "https://doi.org/10.1111/1748-8583.12511", + "abstract": "Abstract Despite frequent claims that increased use of artificial intelligence (AI) in hiring will reduce the human bias that has long plagued recruitment and selection, AI may equally replicate and amplify such bias and embed it in technology. This article explores exclusion and inclusion in AI\u2010supported hiring, focusing on three interrelated areas: data, design and decisions. It is suggested that in terms of data, organisational fit, categorisations and intersectionality require consideration in relation to exclusion. As various stakeholders collaborate to create AI, it is essential to explore which groups are dominant and how subjective assessments are encoded in technology. Although AI\u2010supported hiring should enhance recruitment decisions, evidence is lacking on how humans and machines interact in decision\u2010making, and how algorithms can be audited and regulated effectively for inclusion. This article recommends areas for interrogation through further research, and contributes to understanding how algorithmic inclusion can be achieved in AI\u2010supported hiring.", + "citation_count": 79, + "topics": [ + "Ethics and Social Impacts of AI", + "Employer Branding and e-HRM", + "Digital Economy and Work Transformation" + ], + "discovered_via": [ + "query:algorithmic hiring employment discrimination automated decision systems law regulation audit privacy" + ], + "relevance_score": 8.0, + "relevance_reason": "Directly analyzes bias, inclusion, data, design, human\u2013AI decisions, auditing, and regulation in AI-supported hiring, though it mainly proposes research directions rather than outcome evidence.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "accepted_at": "2026-06-20T09:55:51.437095+00:00", + "primary_category": "Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor", + "categories": [], + "notes": "" + }, + { + "id": "openalex:W4390880618", + "title": "A UTAUT-Based Framework for Analyzing Users\u2019 Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand", + "year": 2024, + "url": "https://doi.org/10.3390/systems12010028", + "doi": "https://doi.org/10.3390/systems12010028", + "abstract": "Recruitment is a fundamental aspect of Human Resource Management to drive organizational performance. Traditional recruitment processes, with manual stages, are time-consuming and inefficient. Artificial Intelligence (AI), which demonstrates its potential in various sectors such as healthcare, education, and notable cases of ChatGPT, is currently reshaping recruitment by automating tasks to improve efficiency. However, in Thailand, where there is a growing demand for talents, the application of AI in recruitment remains relatively limited. This study focuses on human resources (HR) and recruitment professionals in Thailand, aiming to understand their perspectives on the integration of AI in recruitment. It utilized the Unified Theory for Acceptance and Use of Technology (UTAUT) model, customized to suit the specific requirements of Thailand recruitment practices. The study explores the factors influencing users\u2019 intention to adopt AI in recruitment. Survey questionnaire items were created based on prior literature and refined with insights from HR and recruitment experts to ensure applicability in the context of recruitment in Thailand. A survey involving 364 HR and recruiting professionals in the Bangkok metropolitan area supplied comprehensive responses. The study reveals that several factors, including perceived value, perceived autonomy, effort expectancy, and facilitating conditions, significantly impact the intention to adopt AI for recruitment. While social influence and trust in AI technology do not have a direct influence on intention, social influence directly affects perceived value. Trust in AI technology positively influences Effort Expectancy. This study provides valuable benefits for HR and recruitment professionals, organizations, and AI developers by offering insights into AI adoption and sustainability, enhancing recruitment processes and promoting the effective use of AI tools in this sector.", + "citation_count": 80, + "topics": [ + "Employer Branding and e-HRM", + "AI and HR Technologies", + "Technology Adoption and User Behaviour" + ], + "discovered_via": [ + "query:algorithmic hiring survey employers recruiters applicants adoption trust attitudes" + ], + "relevance_score": 7.0, + "relevance_reason": "Provides hiring-specific survey evidence from Thailand on recruiter adoption of AI, including value, autonomy, effort, facilitating conditions, and trust.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "accepted_at": "2026-06-20T09:55:51.437095+00:00", + "primary_category": "Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance", + "categories": [], + "notes": "" + } + ] +} diff --git a/examples/ai-in-hiring-processes/data/papers.json b/examples/ai-in-hiring-processes/data/papers.json new file mode 100644 index 0000000..98346ac --- /dev/null +++ b/examples/ai-in-hiring-processes/data/papers.json @@ -0,0 +1,312 @@ +{ + "topic": "AI in Hiring Processes", + "papers": [ + { + "id": "openalex:1", + "title": "Formal proof search", + "year": 2025, + "url": "https://example.com", + "doi": null, + "abstract": "Proof search with verifier feedback.", + "citation_count": 10, + "topics": [ + "proof search" + ], + "discovered_via": [ + "query:q" + ], + "relevance_score": 8.0, + "relevance_reason": "aligned", + "accepted_at": "2026-06-20T09:31:25.347504+00:00", + "primary_category": "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication", + "categories": [], + "notes": "" + }, + { + "id": "openalex:W4409825334", + "title": "Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity", + "year": 2025, + "url": "https://doi.org/10.3389/fpsyg.2025.1564979", + "doi": "https://doi.org/10.3389/fpsyg.2025.1564979", + "abstract": "This paper explores the efficacy of AI-driven chatbots in accurately inferring personality traits compared to traditional psychometric tests within a real-world professional hiring context. The study is driven by the increasing integration of AI tools in recruitment processes, which necessitates a deeper understanding of their reliability and validity. Using a quasi-experimental design with propensity score matching, we analysed data from 159 candidates and other professionals from Serbian and Montenegrin regions who completed both traditional psychometric assessments and AI-based personality evaluations based on the Big Five Personality model. A novel one-question-per-facet approach was employed in the chatbot assessments with a goal of enabling more granular analysis of the chatbot's psychometric properties. The findings indicate that the chatbot demonstrated good structural, substantive, and convergent validity for certain traits, particularly Extraversion and Conscientiousness, but not for Neuroticism, Agreeableness, and Openness. While robust regression confirmed that AI-inferred scores are less susceptible to social desirability bias than traditional tests, they did not significantly predict real-world outcomes, indicating issues with external validity, particularly predictive validity. The results suggest that AI-driven chatbots show promise for identifying certain personality traits and demonstrate resistance to social desirability bias. This paper contributes to the emerging field of AI and psychometrics by offering insights into the potential and limitations of AI tools in professional selection, while developing an approach for refining psychometric properties of AI-driven assessments.", + "citation_count": 2, + "topics": [ + "Personality Traits and Psychology", + "Employer Branding and e-HRM", + "AI in Service Interactions" + ], + "discovered_via": [ + "query:AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability" + ], + "relevance_score": 10.0, + "relevance_reason": "Direct hiring-context comparison of AI chatbot assessments and psychometric tests, with concrete evidence on validity, reliability, predictive performance, and social-desirability bias.", + "heuristic_relevance_score": 1.2, + "heuristic_relevance_reason": "include=none; topic-token-hits=2; exclude=none", + "accepted_at": "2026-06-20T09:55:51.437095+00:00", + "primary_category": "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication", + "categories": [], + "notes": "" + }, + { + "id": "openalex:W4404228668", + "title": "Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE)", + "year": 2024, + "url": "https://doi.org/10.56294/dm2025487", + "doi": "https://doi.org/10.56294/dm2025487", + "abstract": "IntroductionThis research aims to explore the effectiveness and inclusivity of AI-powered recruitment tools in hiring people with disabilities within the United Arab Emirates. Such is the situation where AI integration into the arena of recruitment is increasingly rapid, while there are vital issues on the side of bias, accessibility, and fairness for applicants of diverse needs. MethodsThis study was a mixed-methods approach, examining sentiment analysis, emotion detection, and HR analytics of feedback from applicants with a disability, 415 in total. The research focused on scores referring to sentiment, the progression rate, and the outcome of the final hiring. ResultsThe sentiment score varied significantly across disability types (p-value <0.05). The applicants with cognitive disability expressed the highest sentiment sore while applicants with hearing impairment had the lowest, which indicated the varying adaptability of AI. The emotion analysis depicted a mix of positive and negative emotions. A few applicants liked technology and have trust in it, while others report fear. Clearly, the applicants, both disabled and non-disabled did not differ in their rate of progression (p-value >0.05), hence never indicating any significant difference within the initial steps of the process. The final hiring stage showed significant differences in results with (p-value <0.05), where the proportionate number of disabled applicants was recorded to be lower than that of non-disabled applicants", + "citation_count": 110, + "topics": [ + "Organizational and Employee Performance", + "AI in Service Interactions", + "Cyberloafing and Workplace Behavior" + ], + "discovered_via": [ + "query:algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability" + ], + "relevance_score": 9.0, + "relevance_reason": "Direct mixed-methods evaluation of accessibility, fairness, applicant experience, progression, and hiring outcomes for disabled applicants using AI recruitment tools in the UAE.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "accepted_at": "2026-06-20T09:55:51.437095+00:00", + "primary_category": "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication", + "categories": [], + "notes": "" + }, + { + "id": "openalex:W4394590365", + "title": "Fairness, AI & recruitment", + "year": 2024, + "url": "https://doi.org/10.1016/j.clsr.2024.105966", + "doi": "https://doi.org/10.1016/j.clsr.2024.105966", + "abstract": "The ever-increasing adoption of AI technologies in the hiring landscape to enhance human resources efficiency raises questions about algorithmic decision-making's implications in employment, especially for job applicants, including those at higher risk of social discrimination. Among other concepts, such as transparency and accountability, fairness has become crucial in AI recruitment debates due to the potential reproduction of bias and discrimination that can disproportionately affect certain vulnerable groups. However, the ideals and ambitions of fairness may signify different meanings to various stakeholders. Conceptualizing fairness is critical because it may provide a clear benchmark for evaluating and mitigating biases, ensuring that AI systems do not perpetuate existing imbalances and promote, in this case, equitable opportunities for all candidates in the job market. To this end, in this article, we conduct a scoping literature review on fairness in AI applications for recruitment and selection purposes, with special emphasis on its definition, categorization, and practical implementation. We start by explaining how AI applications have been increasingly used in the hiring process, especially to increase the efficiency of the HR team. We then move to the limitations of this technological innovation, which is known to be at high risk of privacy violations and social discrimination. Against this backdrop, we focus on defining and operationalizing fairness in AI applications for recruitment and selection purposes through cross-disciplinary lenses. Although the applicable legal frameworks and some research currently address the issue piecemeal, we observe and welcome the emergence of some cross-disciplinary efforts aimed at tackling this multifaceted challenge. We conclude the article with some brief recommendations to guide and shape future research and action on the fairness of AI applications in the hiring process for the better.", + "citation_count": 77, + "topics": [ + "Employer Branding and e-HRM", + "Ethics and Social Impacts of AI", + "Names, Identity, and Discrimination Research" + ], + "discovered_via": [ + "query:algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability" + ], + "relevance_score": 9.0, + "relevance_reason": "Direct scoping review of fairness definitions and implementation in AI recruitment and selection, including bias, vulnerable groups, privacy, transparency, accountability, and legal frameworks.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "accepted_at": "2026-06-20T09:55:51.437095+00:00", + "primary_category": "Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security", + "categories": [], + "notes": "" + }, + { + "id": "openalex:W4386714740", + "title": "Ethics and discrimination in artificial intelligence-enabled recruitment practices", + "year": 2023, + "url": "https://doi.org/10.1057/s41599-023-02079-x", + "doi": "https://doi.org/10.1057/s41599-023-02079-x", + "abstract": "Abstract This study aims to address the research gap on algorithmic discrimination caused by AI-enabled recruitment and explore technical and managerial solutions. The primary research approach used is a literature review. The findings suggest that AI-enabled recruitment has the potential to enhance recruitment quality, increase efficiency, and reduce transactional work. However, algorithmic bias results in discriminatory hiring practices based on gender, race, color, and personality traits. The study indicates that algorithmic bias stems from limited raw data sets and biased algorithm designers. To mitigate this issue, it is recommended to implement technical measures, such as unbiased dataset frameworks and improved algorithmic transparency, as well as management measures like internal corporate ethical governance and external oversight. Employing Grounded Theory, the study conducted survey analysis to collect firsthand data on respondents\u2019 experiences and perceptions of AI-driven recruitment applications and discrimination.", + "citation_count": 351, + "topics": [ + "Ethics and Social Impacts of AI", + "Employer Branding and e-HRM", + "AI and HR Technologies" + ], + "discovered_via": [ + "query:artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection" + ], + "relevance_score": 8.0, + "relevance_reason": "Directly addresses AI recruitment efficiency, discriminatory outcomes, biased data and design, transparency, governance, and external oversight using review and survey evidence.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "accepted_at": "2026-06-20T09:55:51.437095+00:00", + "primary_category": "Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor", + "categories": [], + "notes": "" + }, + { + "id": "openalex:W4391598857", + "title": "A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring", + "year": 2024, + "url": "https://doi.org/10.3390/ai5010019", + "doi": "https://doi.org/10.3390/ai5010019", + "abstract": "The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms\u2019 fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society.", + "citation_count": 112, + "topics": [ + "AI and HR Technologies", + "Artificial Intelligence in Healthcare and Education", + "AI in Service Interactions" + ], + "discovered_via": [ + "query:algorithmic hiring employment discrimination automated decision systems law regulation audit privacy" + ], + "relevance_score": 8.0, + "relevance_reason": "Direct systematic review of bias and mitigation in AI-based CV screening, including efficiency, fairness techniques, case studies, and human\u2013machine collaboration.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "accepted_at": "2026-06-20T09:55:51.437095+00:00", + "primary_category": "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication", + "categories": [], + "notes": "" + }, + { + "id": "openalex:W4366989571", + "title": "Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring", + "year": 2023, + "url": "https://doi.org/10.1111/1748-8583.12511", + "doi": "https://doi.org/10.1111/1748-8583.12511", + "abstract": "Abstract Despite frequent claims that increased use of artificial intelligence (AI) in hiring will reduce the human bias that has long plagued recruitment and selection, AI may equally replicate and amplify such bias and embed it in technology. This article explores exclusion and inclusion in AI\u2010supported hiring, focusing on three interrelated areas: data, design and decisions. It is suggested that in terms of data, organisational fit, categorisations and intersectionality require consideration in relation to exclusion. As various stakeholders collaborate to create AI, it is essential to explore which groups are dominant and how subjective assessments are encoded in technology. Although AI\u2010supported hiring should enhance recruitment decisions, evidence is lacking on how humans and machines interact in decision\u2010making, and how algorithms can be audited and regulated effectively for inclusion. This article recommends areas for interrogation through further research, and contributes to understanding how algorithmic inclusion can be achieved in AI\u2010supported hiring.", + "citation_count": 79, + "topics": [ + "Ethics and Social Impacts of AI", + "Employer Branding and e-HRM", + "Digital Economy and Work Transformation" + ], + "discovered_via": [ + "query:algorithmic hiring employment discrimination automated decision systems law regulation audit privacy" + ], + "relevance_score": 8.0, + "relevance_reason": "Directly analyzes bias, inclusion, data, design, human\u2013AI decisions, auditing, and regulation in AI-supported hiring, though it mainly proposes research directions rather than outcome evidence.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "accepted_at": "2026-06-20T09:55:51.437095+00:00", + "primary_category": "Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor", + "categories": [], + "notes": "" + }, + { + "id": "openalex:W4390880618", + "title": "A UTAUT-Based Framework for Analyzing Users\u2019 Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand", + "year": 2024, + "url": "https://doi.org/10.3390/systems12010028", + "doi": "https://doi.org/10.3390/systems12010028", + "abstract": "Recruitment is a fundamental aspect of Human Resource Management to drive organizational performance. Traditional recruitment processes, with manual stages, are time-consuming and inefficient. Artificial Intelligence (AI), which demonstrates its potential in various sectors such as healthcare, education, and notable cases of ChatGPT, is currently reshaping recruitment by automating tasks to improve efficiency. However, in Thailand, where there is a growing demand for talents, the application of AI in recruitment remains relatively limited. This study focuses on human resources (HR) and recruitment professionals in Thailand, aiming to understand their perspectives on the integration of AI in recruitment. It utilized the Unified Theory for Acceptance and Use of Technology (UTAUT) model, customized to suit the specific requirements of Thailand recruitment practices. The study explores the factors influencing users\u2019 intention to adopt AI in recruitment. Survey questionnaire items were created based on prior literature and refined with insights from HR and recruitment experts to ensure applicability in the context of recruitment in Thailand. A survey involving 364 HR and recruiting professionals in the Bangkok metropolitan area supplied comprehensive responses. The study reveals that several factors, including perceived value, perceived autonomy, effort expectancy, and facilitating conditions, significantly impact the intention to adopt AI for recruitment. While social influence and trust in AI technology do not have a direct influence on intention, social influence directly affects perceived value. Trust in AI technology positively influences Effort Expectancy. This study provides valuable benefits for HR and recruitment professionals, organizations, and AI developers by offering insights into AI adoption and sustainability, enhancing recruitment processes and promoting the effective use of AI tools in this sector.", + "citation_count": 80, + "topics": [ + "Employer Branding and e-HRM", + "AI and HR Technologies", + "Technology Adoption and User Behaviour" + ], + "discovered_via": [ + "query:algorithmic hiring survey employers recruiters applicants adoption trust attitudes" + ], + "relevance_score": 7.0, + "relevance_reason": "Provides hiring-specific survey evidence from Thailand on recruiter adoption of AI, including value, autonomy, effort, facilitating conditions, and trust.", + "heuristic_relevance_score": 0.6, + "heuristic_relevance_reason": "include=none; topic-token-hits=1; exclude=none", + "accepted_at": "2026-06-20T09:55:51.437095+00:00", + "primary_category": "Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance", + "categories": [], + "notes": "" + } + ], + "scout_runs": [ + { + "date": "2026-06-19", + "queries": [ + "artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection", + "AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability", + "AI recruitment system human in the loop recruiter decision support workflow architecture monitoring", + "algorithmic hiring employment discrimination automated decision systems law regulation audit privacy", + "AI recruiting business value time to hire cost per hire quality of hire recruiter productivity ROI", + "algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability", + "AI hiring adversarial gaming prompt injection resume manipulation deepfake interview fraud automation bias model drift", + "generative AI recruiting systems candidate communication job descriptions interview assistants", + "algorithmic hiring survey employers recruiters applicants adoption trust attitudes", + "AI hiring methods causal evaluation field experiment longitudinal quality of hire" + ], + "accepted_ids": [], + "accepted_count": 0, + "candidate_count": 540, + "cost": { + "provider": "openalex", + "token_count": 0, + "money_cost_usd": 0.0, + "currency": "USD", + "note": "OpenAlex discovery does not invoke a model; token usage is zero." + } + }, + { + "date": "2026-06-19", + "queries": [ + "artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection", + "AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability", + "AI recruitment system human in the loop recruiter decision support workflow architecture monitoring", + "algorithmic hiring employment discrimination automated decision systems law regulation audit privacy", + "AI recruiting business value time to hire cost per hire quality of hire recruiter productivity ROI", + "algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability", + "AI hiring adversarial gaming prompt injection resume manipulation deepfake interview fraud automation bias model drift", + "generative AI recruiting systems candidate communication job descriptions interview assistants", + "algorithmic hiring survey employers recruiters applicants adoption trust attitudes", + "AI hiring methods causal evaluation field experiment longitudinal quality of hire" + ], + "accepted_ids": [], + "accepted_count": 0, + "candidate_count": 540, + "cost": { + "provider": "codex", + "model": "codex-cli:configured-model", + "token_count": 40597, + "input_tokens": 38704, + "output_tokens": 1849, + "reasoning_tokens": 44, + "money_cost_usd": 0.0, + "currency": "USD", + "note": "Codex CLI reports token usage but does not expose billable USD cost in this workflow." + } + }, + { + "date": "2026-06-20", + "queries": [ + "q" + ], + "accepted_ids": [ + "openalex:1" + ], + "accepted_count": 1, + "candidate_count": 1, + "cost": { + "provider": "openalex", + "token_count": 0, + "money_cost_usd": 0.0, + "currency": "USD", + "note": "OpenAlex discovery does not invoke a model; token usage is zero." + } + }, + { + "date": "2026-06-20", + "queries": [ + "artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection", + "AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability", + "AI recruitment system human in the loop recruiter decision support workflow architecture monitoring", + "algorithmic hiring employment discrimination automated decision systems law regulation audit privacy", + "AI recruiting business value time to hire cost per hire quality of hire recruiter productivity ROI", + "algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability", + "AI hiring adversarial gaming prompt injection resume manipulation deepfake interview fraud automation bias model drift", + "generative AI recruiting systems candidate communication job descriptions interview assistants", + "algorithmic hiring survey employers recruiters applicants adoption trust attitudes", + "AI hiring methods causal evaluation field experiment longitudinal quality of hire" + ], + "accepted_ids": [ + "openalex:W4409825334", + "openalex:W4404228668", + "openalex:W4394590365", + "openalex:W4386714740", + "openalex:W4391598857", + "openalex:W4366989571", + "openalex:W4390880618" + ], + "accepted_count": 7, + "candidate_count": 540, + "cost": { + "provider": "codex", + "model": "codex-cli:configured-model", + "token_count": 40624, + "input_tokens": 38704, + "output_tokens": 1876, + "reasoning_tokens": 44, + "money_cost_usd": 0.0, + "currency": "USD", + "note": "Codex CLI reports token usage but does not expose billable USD cost in this workflow." + } + } + ] +} diff --git a/examples/ai-in-hiring-processes/data/research_opportunities.example.json b/examples/ai-in-hiring-processes/data/research_opportunities.example.json new file mode 100644 index 0000000..a12a10a --- /dev/null +++ b/examples/ai-in-hiring-processes/data/research_opportunities.example.json @@ -0,0 +1,70 @@ +{ + "generated_at": "YYYY-MM-DD", + "analysis_model": "agent/model", + "summary": "Evidence-backed summary of the largest missing areas.", + "opportunities": [ + { + "rank": 1, + "title": "Specific corpus opportunity", + "gap_type": "corpus_gap", + "priority_score": 90, + "confidence": "medium", + "scope": "What is missing.", + "evidence": [ + {"observation": "Observed corpus fact.", "source": "data/papers.json"}, + {"observation": "Observed graph or report fact.", "source": "data/dashboard.json"} + ], + "llm_reasoning": "Inference from the cited evidence.", + "uncertainty": "What may make the inference incomplete.", + "coverage_check": { + "query_terms": ["term one"], + "matched_titles": [], + "interpretation": "A bounded corpus claim, not a field-wide absence claim." + }, + "research_questions": ["Question one?", "Question two?"], + "scout_queries": ["targeted query one", "targeted query two"] + }, + { + "rank": 2, + "title": "Specific evidence opportunity", + "gap_type": "evidence_gap", + "priority_score": 80, + "confidence": "medium", + "scope": "What evidence is weak.", + "evidence": [ + {"observation": "Observed corpus fact.", "source": "data/papers.json"}, + {"observation": "Observed report fact.", "source": "reports/research_report.md"} + ], + "llm_reasoning": "Inference from the cited evidence.", + "uncertainty": "Possible missing evidence.", + "coverage_check": { + "query_terms": ["term two"], + "matched_titles": ["Example title"], + "interpretation": "The corpus has proposals but limited validation." + }, + "research_questions": ["Question one?", "Question two?"], + "scout_queries": ["targeted query one", "targeted query two"] + }, + { + "rank": 3, + "title": "Specific translation opportunity", + "gap_type": "translation_gap", + "priority_score": 70, + "confidence": "low", + "scope": "What is not operationalized.", + "evidence": [ + {"observation": "Observed corpus fact.", "source": "data/papers.json"}, + {"observation": "Observed workflow fact.", "source": "AGENTS.md"} + ], + "llm_reasoning": "Inference from the cited evidence.", + "uncertainty": "Possible external tools not represented.", + "coverage_check": { + "query_terms": ["term three"], + "matched_titles": ["Example title"], + "interpretation": "The corpus shows research but not a maintained implementation." + }, + "research_questions": ["Question one?", "Question two?"], + "scout_queries": ["targeted query one", "targeted query two"] + } + ] +} diff --git a/examples/ai-in-hiring-processes/data/research_opportunities.json b/examples/ai-in-hiring-processes/data/research_opportunities.json new file mode 100644 index 0000000..9f41969 --- /dev/null +++ b/examples/ai-in-hiring-processes/data/research_opportunities.json @@ -0,0 +1,312 @@ +{ + "generated_at": "2026-06-20T10:46:15.552006+00:00", + "analysis_model": "codex-cli:configured-model", + "summary": "The accepted corpus supports bounded opportunities in human\u2013AI decision workflows, predictive validity, operational value, accessibility, and governance. These are corpus gaps, evidence gaps, or translation gaps\u2014not claims of field-wide absence. One accepted record (\u201cFormal proof search\u201d) appears unrelated to hiring and weakens corpus reliability.", + "opportunities": [ + { + "rank": 1, + "title": "Human\u2013AI decision workflows, overrides, and accountability", + "gap_type": "evidence_gap", + "priority_score": 95, + "confidence": "high", + "scope": "Empirical studies of recruiter or hiring-manager interaction with AI recommendations, including reliance, overrides, escalation, accountability, and resulting hiring outcomes.", + "evidence": [ + { + "observation": "The corpus shape reports zero coverage for decision roles and risk mechanisms.", + "source": "Report excerpt: Corpus Shape" + }, + { + "observation": "The algorithmic inclusion paper explicitly states that evidence is lacking on human\u2013machine interaction in hiring decisions and effective auditing.", + "source": "openalex:W4366989571" + }, + { + "observation": "The Thailand study measures intention to adopt AI but does not report observed decision behavior or hiring outcomes.", + "source": "openalex:W4390880618" + } + ], + "llm_reasoning": "The corpus discusses collaboration, adoption, and oversight conceptually, but provides no accepted empirical study of how recruiters use, reject, or defer to AI recommendations. This is a bounded evidence gap within the accepted corpus.", + "uncertainty": "Relevant empirical studies may exist among rejected candidates or outside the current search/index coverage. The available abstracts may also omit workflow details present in full texts.", + "coverage_check": { + "query_terms": [ + "human AI hiring override behavior", + "recruiter reliance algorithmic recommendations", + "automation bias recruitment field experiment", + "hiring escalation accountability AI" + ], + "matched_titles": [ + "Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring", + "A UTAUT-Based Framework for Analyzing Users\u2019 Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand", + "Ethics and discrimination in artificial intelligence-enabled recruitment practices" + ], + "interpretation": "Matched papers motivate human oversight or adoption, but the supplied evidence does not establish observed override behavior, automation bias, or accountability outcomes." + }, + "research_questions": [ + "How often do recruiters override AI recommendations, and under what conditions?", + "How do explanations, confidence scores, and review requirements affect reliance and decision quality?", + "Who is accountable when human and algorithmic recommendations conflict?", + "Do human-review controls reduce adverse impact without eliminating efficiency gains?" + ], + "scout_queries": [ + "(\"algorithmic hiring\" OR \"AI recruitment\") recruiter override reliance experiment", + "(\"automation bias\" OR algorithm aversion) hiring recruitment selection", + "human-in-the-loop hiring decision support field study accountability", + "AI hiring recommendation disagreement escalation recruiter" + ] + }, + { + "rank": 2, + "title": "Predictive validity and generalization across jobs and populations", + "gap_type": "evidence_gap", + "priority_score": 92, + "confidence": "high", + "scope": "Independent, job-related validation of AI screening and assessment systems across occupations, applicant populations, locations, and time periods.", + "evidence": [ + { + "observation": "The chatbot study reports validity for some personality traits but not others, and reports no significant prediction of real-world outcomes.", + "source": "openalex:W4409825334" + }, + { + "observation": "The chatbot sample contains 159 candidates and professionals from Serbian and Montenegrin regions.", + "source": "openalex:W4409825334" + }, + { + "observation": "The remaining accepted papers primarily address fairness, adoption, ethics, or inclusion rather than criterion-related prediction of job performance or hiring success.", + "source": "Accepted-paper corpus" + } + ], + "llm_reasoning": "The only accepted paper supplying direct predictive-validity evidence reports weak external validity in a geographically bounded sample. The corpus therefore cannot support general claims about quality-of-hire or generalization.", + "uncertainty": "Full texts of other accepted papers may contain limited validation evidence not visible in the abstracts. Relevant validation studies may also use product-specific terminology absent from current queries.", + "coverage_check": { + "query_terms": [ + "predictive validity AI hiring", + "criterion validity algorithmic assessment", + "quality of hire AI longitudinal", + "generalization across jobs populations" + ], + "matched_titles": [ + "Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity" + ], + "interpretation": "Only one supplied match directly evaluates predictive validity, and its results are mixed and context-specific." + }, + "research_questions": [ + "Which AI-derived hiring scores predict job performance, retention, or training outcomes beyond established assessments?", + "How stable are validity coefficients across occupations, languages, applicant groups, and labor markets?", + "How do model updates and distribution shifts affect criterion validity over time?", + "What sample sizes and validation designs are required for credible deployment decisions?" + ], + "scout_queries": [ + "(\"AI hiring\" OR \"algorithmic selection\") \"criterion-related validity\"", + "(\"machine learning\" recruitment assessment) predictive validity job performance", + "AI hiring longitudinal \"quality of hire\" validation", + "algorithmic assessment cross-validation occupation population generalization" + ] + }, + { + "rank": 3, + "title": "Measured operational value and quality trade-offs", + "gap_type": "evidence_gap", + "priority_score": 89, + "confidence": "high", + "scope": "Field experiments or longitudinal evaluations measuring time-to-hire, cost-per-hire, recruiter workload, conversion, quality-of-hire, and fairness jointly.", + "evidence": [ + { + "observation": "The topic contract identifies time-to-hire, cost-per-hire, recruiter workload, conversion, and quality-of-hire as dashboard metrics.", + "source": "Topic contract" + }, + { + "observation": "The ethics paper states that AI may increase efficiency and reduce transactional work, but the supplied abstract does not provide quantified operational outcomes.", + "source": "openalex:W4386714740" + }, + { + "observation": "The Thailand paper studies perceived value and adoption intention rather than realized business metrics.", + "source": "openalex:W4390880618" + }, + { + "observation": "The corpus shape reports only one paper under evaluation dimensions.", + "source": "Report excerpt: Corpus Shape" + } + ], + "llm_reasoning": "Operational benefits are asserted or perceived in the accepted corpus, but the supplied evidence does not quantify realized value or connect efficiency gains to validity, fairness, and candidate outcomes.", + "uncertainty": "The abstracts may omit numerical results available in full papers. Vendor or internal organizational studies may exist but may not meet the contract\u2019s evidence and independence requirements.", + "coverage_check": { + "query_terms": [ + "time to hire AI recruitment", + "cost per hire AI", + "recruiter workload field experiment", + "quality of hire longitudinal" + ], + "matched_titles": [ + "Ethics and discrimination in artificial intelligence-enabled recruitment practices", + "A UTAUT-Based Framework for Analyzing Users\u2019 Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand" + ], + "interpretation": "The supplied matches discuss efficiency or perceived value, not controlled measurements of realized business value and downstream quality." + }, + "research_questions": [ + "What causal effect does AI assistance have on time-to-hire and recruiter workload?", + "Do efficiency gains persist after including review, audit, appeal, and monitoring costs?", + "How do operational gains trade off against predictive validity, fairness, and candidate experience?", + "Which hiring stages produce measurable value under assistive versus semi-automated deployment?" + ], + "scout_queries": [ + "AI recruitment field experiment time-to-hire recruiter workload", + "algorithmic hiring cost-per-hire causal evaluation", + "AI screening longitudinal quality-of-hire conversion", + "recruitment automation ROI independent evaluation fairness" + ] + }, + { + "rank": 4, + "title": "Accessibility mechanisms and stage-specific outcomes for disabled applicants", + "gap_type": "evidence_gap", + "priority_score": 84, + "confidence": "high", + "scope": "Stage-level studies of accessibility barriers, accommodations, disability-specific measurement validity, progression, final selection, and appeal mechanisms.", + "evidence": [ + { + "observation": "The UAE study reports different sentiment across disability types and lower final hiring outcomes for disabled applicants despite no significant difference in initial progression.", + "source": "openalex:W4404228668" + }, + { + "observation": "The supplied corpus contains no second disability-focused accepted paper for replication or comparison.", + "source": "Accepted-paper corpus" + }, + { + "observation": "The fairness review emphasizes vulnerable groups but the supplied abstract does not provide disability-specific stage-level outcome evidence.", + "source": "openalex:W4394590365" + } + ], + "llm_reasoning": "The UAE study identifies a stage-dependent disparity but does not, from the supplied abstract, isolate whether accessibility, measurement validity, workflow design, or human selection decisions caused it. Replication and mechanism-focused evidence are needed before interpreting the result.", + "uncertainty": "The full UAE paper may contain additional controls or tool descriptions. Results may not generalize beyond the UAE, the examined tools, or represented disability categories.", + "coverage_check": { + "query_terms": [ + "disabled applicants AI hiring accessibility", + "reasonable accommodation algorithmic assessment", + "disability adverse impact recruitment AI", + "accessible automated interview" + ], + "matched_titles": [ + "Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE)", + "Fairness, AI & recruitment" + ], + "interpretation": "One empirical paper provides disability-specific outcomes; the supplied corpus lacks comparative evidence across tools, jurisdictions, stages, and accommodation designs." + }, + "research_questions": [ + "At which hiring stage do disability-related outcome differences emerge, and through which mechanism?", + "Are AI assessment constructs and interfaces valid across disability types and assistive technologies?", + "How do accommodation, disclosure, human review, and appeal procedures alter outcomes?", + "Do accessibility improvements affect predictive validity or false-negative rates?" + ], + "scout_queries": [ + "(\"AI hiring\" OR \"automated employment decision\") disability accessibility outcomes", + "algorithmic recruitment reasonable accommodation assistive technology", + "automated interview disability validation adverse impact", + "AI employment assessment disabled applicants appeal human review" + ] + }, + { + "rank": 5, + "title": "Operationalizing fairness into auditable controls", + "gap_type": "translation_gap", + "priority_score": 82, + "confidence": "high", + "scope": "Implementation and evaluation of fairness definitions through documentation, subgroup testing, audit procedures, monitoring thresholds, remediation, appeals, and vendor assurance.", + "evidence": [ + { + "observation": "The fairness review states that fairness has different meanings across stakeholders and focuses on definition, categorization, and practical implementation.", + "source": "openalex:W4394590365" + }, + { + "observation": "The bias-mitigation review identifies vector-space correction and data augmentation as mitigation techniques.", + "source": "openalex:W4391598857" + }, + { + "observation": "The ethics paper recommends dataset, transparency, internal governance, and external oversight measures.", + "source": "openalex:W4386714740" + }, + { + "observation": "The corpus shape reports only one governance-control paper, whose supplied evidence concerns adoption intention rather than control effectiveness.", + "source": "Report excerpt and openalex:W4390880618" + } + ], + "llm_reasoning": "The corpus proposes fairness concepts and mitigation methods, but the supplied evidence does not show how organizations select fairness criteria, establish audit thresholds, respond to failures, or verify that controls improve outcomes in deployment.", + "uncertainty": "Detailed implementation guidance may appear in the full review articles. Jurisdiction-specific standards and audits may be indexed under legal or technical terminology not captured by the accepted corpus.", + "coverage_check": { + "query_terms": [ + "algorithmic hiring fairness audit implementation", + "adverse impact monitoring AI recruitment", + "vendor assurance automated hiring", + "appeal contestability hiring algorithm" + ], + "matched_titles": [ + "Fairness, AI & recruitment", + "A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring", + "Ethics and discrimination in artificial intelligence-enabled recruitment practices", + "Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring" + ], + "interpretation": "The supplied papers identify fairness and governance needs, but do not provide accepted evidence of deployed control effectiveness or repeatable audit workflows." + }, + "research_questions": [ + "How should employers choose fairness metrics for specific hiring stages and legal contexts?", + "Which audit and monitoring controls detect meaningful adverse impact after deployment?", + "How effective are remediation methods under intersectional and distribution-shift conditions?", + "What evidence should vendors provide to employers, regulators, applicants, and auditors?" + ], + "scout_queries": [ + "algorithmic hiring audit protocol adverse impact monitoring", + "AI recruitment fairness metrics implementation case study", + "automated employment decision tool vendor audit assurance", + "algorithmic hiring appeal contestability remediation monitoring" + ] + }, + { + "rank": 6, + "title": "Corpus integrity and false-positive acceptance control", + "gap_type": "corpus_gap", + "priority_score": 98, + "confidence": "high", + "scope": "Validate accepted-paper relevance, identifiers, URLs, and provenance before using corpus counts or gap conclusions.", + "evidence": [ + { + "observation": "\u201cFormal proof search\u201d has the abstract \u201cProof search with verifier feedback,\u201d topic \u201cproof search,\u201d and no stated connection to hiring.", + "source": "openalex:1" + }, + { + "observation": "Its source is listed as https://example.com rather than a scholarly or stable source URL.", + "source": "Report excerpt: Papers" + }, + { + "observation": "The record was accepted from a scout run using only the query \u201cq.\u201d", + "source": "Scout run dated 2026-06-20" + }, + { + "observation": "The research contract requires every paper to retain a source URL and stable identifier and requires rejection of adjacent out-of-scope work.", + "source": "AGENTS.md research contract and hard rules" + } + ], + "llm_reasoning": "This record is inconsistent with the topic contract and appears to be a false positive or test fixture. Because the corpus has only eight accepted records, retaining it materially distorts coverage counts and downstream gap analysis.", + "uncertainty": "The supplied record could represent intentionally injected test data. Its relevance cannot be recovered from the provided metadata.", + "coverage_check": { + "query_terms": [ + "formal proof search hiring recruitment employment selection", + "openalex:1 provenance" + ], + "matched_titles": [ + "Formal proof search" + ], + "interpretation": "The record matches no hiring concept in its title, abstract, or topic metadata and lacks a credible source URL." + }, + "research_questions": [ + "Is openalex:1 a test fixture or an accidental acceptance?", + "Does its identifier resolve to a real scholarly record?", + "Which validation rule allowed a non-hiring paper and placeholder URL to pass acceptance?", + "Should corpus statistics be regenerated after relevance and provenance validation?" + ], + "scout_queries": [ + "validate openalex:1 title abstract source identifier", + "\"Formal proof search\" hiring recruitment", + "accepted corpus placeholder URL example.com audit", + "false positive relevance filter AI hiring corpus" + ] + } + ] +} diff --git a/examples/ai-in-hiring-processes/data/sequential_tasks.json b/examples/ai-in-hiring-processes/data/sequential_tasks.json new file mode 100644 index 0000000..64c2ea0 --- /dev/null +++ b/examples/ai-in-hiring-processes/data/sequential_tasks.json @@ -0,0 +1,111 @@ +{ + "mode": "sequential", + "topic": "AI in Hiring Processes", + "coordinator_instruction": "Read AGENTS.md and topic.json. Assign only accepted research-contract tasks. Do not publish when no new paper is accepted.", + "tasks": [ + { + "id": "queries", + "role": "query_designer", + "objective": "Review topic.json and improve search_queries without broadening excluded scope.", + "inputs": [ + "topic.json" + ], + "outputs": [ + "topic.json" + ], + "depends_on": [] + }, + { + "id": "discover", + "role": "graph_scout", + "objective": "Run scholarly graph discovery and produce candidates with provenance.", + "inputs": [ + "topic.json", + "data/papers.json" + ], + "outputs": [ + "data/candidates.json" + ], + "depends_on": [ + "queries" + ] + }, + { + "id": "review", + "role": "relevance_reviewer", + "objective": "Apply inclusion/exclusion rules and propose accepted candidate IDs.", + "inputs": [ + "topic.json", + "data/candidates.json" + ], + "outputs": [ + "review decision" + ], + "depends_on": [ + "discover" + ] + }, + { + "id": "library", + "role": "librarian", + "objective": "Accept approved IDs, deduplicate, and preserve source metadata.", + "inputs": [ + "data/candidates.json", + "review decision" + ], + "outputs": [ + "data/papers.json" + ], + "depends_on": [ + "review" + ] + }, + { + "id": "synthesis", + "role": "analyst", + "objective": "Classify papers and rebuild Markdown notes and synthesis.", + "inputs": [ + "topic.json", + "data/papers.json" + ], + "outputs": [ + "reports/research_report.md", + "reports/papers/" + ], + "depends_on": [ + "library" + ] + }, + { + "id": "gaps", + "role": "gap_analyst", + "objective": "Use skills/analyze-research-gaps/SKILL.md to write evidence-backed opportunities.", + "inputs": [ + "data/papers.json", + "reports/research_report.md" + ], + "outputs": [ + "data/research_opportunities.json" + ], + "depends_on": [ + "synthesis" + ] + }, + { + "id": "publish", + "role": "publisher", + "objective": "Regenerate dashboard, graph, wiki, trends, and opportunity view.", + "inputs": [ + "data/papers.json", + "data/research_opportunities.json" + ], + "outputs": [ + "topic-dashboard.html", + "data/dashboard.json" + ], + "depends_on": [ + "gaps" + ] + } + ] +} diff --git a/examples/ai-in-hiring-processes/data/swarm_tasks.json b/examples/ai-in-hiring-processes/data/swarm_tasks.json new file mode 100644 index 0000000..06fc807 --- /dev/null +++ b/examples/ai-in-hiring-processes/data/swarm_tasks.json @@ -0,0 +1,111 @@ +{ + "mode": "swarm", + "topic": "AI in Hiring Processes", + "coordinator_instruction": "Read AGENTS.md and topic.json. Assign only accepted research-contract tasks. Do not publish when no new paper is accepted.", + "tasks": [ + { + "id": "queries", + "role": "query_designer", + "objective": "Review topic.json and improve search_queries without broadening excluded scope.", + "inputs": [ + "topic.json" + ], + "outputs": [ + "topic.json" + ], + "depends_on": [] + }, + { + "id": "discover", + "role": "graph_scout", + "objective": "Run scholarly graph discovery and produce candidates with provenance.", + "inputs": [ + "topic.json", + "data/papers.json" + ], + "outputs": [ + "data/candidates.json" + ], + "depends_on": [ + "queries" + ] + }, + { + "id": "review", + "role": "relevance_reviewer", + "objective": "Apply inclusion/exclusion rules and propose accepted candidate IDs.", + "inputs": [ + "topic.json", + "data/candidates.json" + ], + "outputs": [ + "review decision" + ], + "depends_on": [ + "discover" + ] + }, + { + "id": "library", + "role": "librarian", + "objective": "Accept approved IDs, deduplicate, and preserve source metadata.", + "inputs": [ + "data/candidates.json", + "review decision" + ], + "outputs": [ + "data/papers.json" + ], + "depends_on": [ + "review" + ] + }, + { + "id": "synthesis", + "role": "analyst", + "objective": "Classify papers and rebuild Markdown notes and synthesis.", + "inputs": [ + "topic.json", + "data/papers.json" + ], + "outputs": [ + "reports/research_report.md", + "reports/papers/" + ], + "depends_on": [ + "library" + ] + }, + { + "id": "gaps", + "role": "gap_analyst", + "objective": "Use skills/analyze-research-gaps/SKILL.md to write evidence-backed opportunities.", + "inputs": [ + "data/papers.json", + "reports/research_report.md" + ], + "outputs": [ + "data/research_opportunities.json" + ], + "depends_on": [ + "synthesis" + ] + }, + { + "id": "publish", + "role": "publisher", + "objective": "Regenerate dashboard, graph, wiki, trends, and opportunity view.", + "inputs": [ + "data/papers.json", + "data/research_opportunities.json" + ], + "outputs": [ + "topic-dashboard.html", + "data/dashboard.json" + ], + "depends_on": [ + "gaps" + ] + } + ] +} diff --git a/examples/ai-in-hiring-processes/governance-dashboard.html b/examples/ai-in-hiring-processes/governance-dashboard.html new file mode 100644 index 0000000..bfe998f --- /dev/null +++ b/examples/ai-in-hiring-processes/governance-dashboard.html @@ -0,0 +1,315 @@ + + + + + + EvaPaper Research Signal + + + +
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EvaPaper / Research intelligence

Agent governance signal.

Topic concentration, layer coverage, and the direction of research discovered across scout runs.

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Latest evidence
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Distribution

What the corpus studies

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Momentum

Cumulative topic growth

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Coverage

Governance layers

Multi-layer papers count in every applicable layer.

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Last three scout windows

Topics gaining attention

Momentum is based on discovery dates, not publication dates.

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Living knowledge base

EvaPaper Wiki

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Knowledge graph

Paper associations

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Drag nodes · drag background to pan · scroll to zoom · click for evidence
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LLM gap analysis

Underexplored areas & research opportunities

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These rankings are reasoned hypotheses over the collected corpus, not proof that no outside literature exists. Each scout update must reassess them using the workspace skill.

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Evidence ledger

Paper inventory

FoundPaperPrimary topicLayers

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+ + + + diff --git a/examples/ai-in-hiring-processes/reports/papers/a-comprehensive-review-of-ai-techniques-for-addressing-algorithmic-bias-in-job-hiring.md b/examples/ai-in-hiring-processes/reports/papers/a-comprehensive-review-of-ai-techniques-for-addressing-algorithmic-bias-in-job-hiring.md new file mode 100644 index 0000000..8a99d7b --- /dev/null +++ b/examples/ai-in-hiring-processes/reports/papers/a-comprehensive-review-of-ai-techniques-for-addressing-algorithmic-bias-in-job-hiring.md @@ -0,0 +1,21 @@ +# A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring + +- **Identifier:** `openalex:W4391598857` +- **Year:** 2024 +- **Primary category:** Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication +- **Source:** https://doi.org/10.3390/ai5010019 +- **Citations at discovery:** 112 + +## Abstract + +The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms’ fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society. + +## Scout Assessment + +- Relevance score: 8.0 +- Reason: Direct systematic review of bias and mitigation in AI-based CV screening, including efficiency, fairness techniques, case studies, and human–machine collaboration. +- Notes: Pending analyst notes. + +## Provenance + +Discovered via: query:algorithmic hiring employment discrimination automated decision systems law regulation audit privacy diff --git a/examples/ai-in-hiring-processes/reports/papers/a-utaut-based-framework-for-analyzing-users-intention-to-adopt-artificial-intelligence-in-.md b/examples/ai-in-hiring-processes/reports/papers/a-utaut-based-framework-for-analyzing-users-intention-to-adopt-artificial-intelligence-in-.md new file mode 100644 index 0000000..268db8b --- /dev/null +++ b/examples/ai-in-hiring-processes/reports/papers/a-utaut-based-framework-for-analyzing-users-intention-to-adopt-artificial-intelligence-in-.md @@ -0,0 +1,21 @@ +# A UTAUT-Based Framework for Analyzing Users’ Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand + +- **Identifier:** `openalex:W4390880618` +- **Year:** 2024 +- **Primary category:** Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance +- **Source:** https://doi.org/10.3390/systems12010028 +- **Citations at discovery:** 80 + +## Abstract + +Recruitment is a fundamental aspect of Human Resource Management to drive organizational performance. Traditional recruitment processes, with manual stages, are time-consuming and inefficient. Artificial Intelligence (AI), which demonstrates its potential in various sectors such as healthcare, education, and notable cases of ChatGPT, is currently reshaping recruitment by automating tasks to improve efficiency. However, in Thailand, where there is a growing demand for talents, the application of AI in recruitment remains relatively limited. This study focuses on human resources (HR) and recruitment professionals in Thailand, aiming to understand their perspectives on the integration of AI in recruitment. It utilized the Unified Theory for Acceptance and Use of Technology (UTAUT) model, customized to suit the specific requirements of Thailand recruitment practices. The study explores the factors influencing users’ intention to adopt AI in recruitment. Survey questionnaire items were created based on prior literature and refined with insights from HR and recruitment experts to ensure applicability in the context of recruitment in Thailand. A survey involving 364 HR and recruiting professionals in the Bangkok metropolitan area supplied comprehensive responses. The study reveals that several factors, including perceived value, perceived autonomy, effort expectancy, and facilitating conditions, significantly impact the intention to adopt AI for recruitment. While social influence and trust in AI technology do not have a direct influence on intention, social influence directly affects perceived value. Trust in AI technology positively influences Effort Expectancy. This study provides valuable benefits for HR and recruitment professionals, organizations, and AI developers by offering insights into AI adoption and sustainability, enhancing recruitment processes and promoting the effective use of AI tools in this sector. + +## Scout Assessment + +- Relevance score: 7.0 +- Reason: Provides hiring-specific survey evidence from Thailand on recruiter adoption of AI, including value, autonomy, effort, facilitating conditions, and trust. +- Notes: Pending analyst notes. + +## Provenance + +Discovered via: query:algorithmic hiring survey employers recruiters applicants adoption trust attitudes diff --git a/examples/ai-in-hiring-processes/reports/papers/algorithmic-inclusion-shaping-the-predictive-algorithms-of-artificial-intelligence-in-hiri.md b/examples/ai-in-hiring-processes/reports/papers/algorithmic-inclusion-shaping-the-predictive-algorithms-of-artificial-intelligence-in-hiri.md new file mode 100644 index 0000000..87bf47d --- /dev/null +++ b/examples/ai-in-hiring-processes/reports/papers/algorithmic-inclusion-shaping-the-predictive-algorithms-of-artificial-intelligence-in-hiri.md @@ -0,0 +1,21 @@ +# Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring + +- **Identifier:** `openalex:W4366989571` +- **Year:** 2023 +- **Primary category:** Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor +- **Source:** https://doi.org/10.1111/1748-8583.12511 +- **Citations at discovery:** 79 + +## Abstract + +Abstract Despite frequent claims that increased use of artificial intelligence (AI) in hiring will reduce the human bias that has long plagued recruitment and selection, AI may equally replicate and amplify such bias and embed it in technology. This article explores exclusion and inclusion in AI‐supported hiring, focusing on three interrelated areas: data, design and decisions. It is suggested that in terms of data, organisational fit, categorisations and intersectionality require consideration in relation to exclusion. As various stakeholders collaborate to create AI, it is essential to explore which groups are dominant and how subjective assessments are encoded in technology. Although AI‐supported hiring should enhance recruitment decisions, evidence is lacking on how humans and machines interact in decision‐making, and how algorithms can be audited and regulated effectively for inclusion. This article recommends areas for interrogation through further research, and contributes to understanding how algorithmic inclusion can be achieved in AI‐supported hiring. + +## Scout Assessment + +- Relevance score: 8.0 +- Reason: Directly analyzes bias, inclusion, data, design, human–AI decisions, auditing, and regulation in AI-supported hiring, though it mainly proposes research directions rather than outcome evidence. +- Notes: Pending analyst notes. + +## Provenance + +Discovered via: query:algorithmic hiring employment discrimination automated decision systems law regulation audit privacy diff --git a/examples/ai-in-hiring-processes/reports/papers/comparing-chatbots-to-psychometric-tests-in-hiring-reduced-social-desirability-bias-but-lo.md b/examples/ai-in-hiring-processes/reports/papers/comparing-chatbots-to-psychometric-tests-in-hiring-reduced-social-desirability-bias-but-lo.md new file mode 100644 index 0000000..1ee3511 --- /dev/null +++ b/examples/ai-in-hiring-processes/reports/papers/comparing-chatbots-to-psychometric-tests-in-hiring-reduced-social-desirability-bias-but-lo.md @@ -0,0 +1,21 @@ +# Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity + +- **Identifier:** `openalex:W4409825334` +- **Year:** 2025 +- **Primary category:** Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication +- **Source:** https://doi.org/10.3389/fpsyg.2025.1564979 +- **Citations at discovery:** 2 + +## Abstract + +This paper explores the efficacy of AI-driven chatbots in accurately inferring personality traits compared to traditional psychometric tests within a real-world professional hiring context. The study is driven by the increasing integration of AI tools in recruitment processes, which necessitates a deeper understanding of their reliability and validity. Using a quasi-experimental design with propensity score matching, we analysed data from 159 candidates and other professionals from Serbian and Montenegrin regions who completed both traditional psychometric assessments and AI-based personality evaluations based on the Big Five Personality model. A novel one-question-per-facet approach was employed in the chatbot assessments with a goal of enabling more granular analysis of the chatbot's psychometric properties. The findings indicate that the chatbot demonstrated good structural, substantive, and convergent validity for certain traits, particularly Extraversion and Conscientiousness, but not for Neuroticism, Agreeableness, and Openness. While robust regression confirmed that AI-inferred scores are less susceptible to social desirability bias than traditional tests, they did not significantly predict real-world outcomes, indicating issues with external validity, particularly predictive validity. The results suggest that AI-driven chatbots show promise for identifying certain personality traits and demonstrate resistance to social desirability bias. This paper contributes to the emerging field of AI and psychometrics by offering insights into the potential and limitations of AI tools in professional selection, while developing an approach for refining psychometric properties of AI-driven assessments. + +## Scout Assessment + +- Relevance score: 10.0 +- Reason: Direct hiring-context comparison of AI chatbot assessments and psychometric tests, with concrete evidence on validity, reliability, predictive performance, and social-desirability bias. +- Notes: Pending analyst notes. + +## Provenance + +Discovered via: query:AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability diff --git a/examples/ai-in-hiring-processes/reports/papers/ethics-and-discrimination-in-artificial-intelligence-enabled-recruitment-practices.md b/examples/ai-in-hiring-processes/reports/papers/ethics-and-discrimination-in-artificial-intelligence-enabled-recruitment-practices.md new file mode 100644 index 0000000..3ccd9b1 --- /dev/null +++ b/examples/ai-in-hiring-processes/reports/papers/ethics-and-discrimination-in-artificial-intelligence-enabled-recruitment-practices.md @@ -0,0 +1,21 @@ +# Ethics and discrimination in artificial intelligence-enabled recruitment practices + +- **Identifier:** `openalex:W4386714740` +- **Year:** 2023 +- **Primary category:** Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor +- **Source:** https://doi.org/10.1057/s41599-023-02079-x +- **Citations at discovery:** 351 + +## Abstract + +Abstract This study aims to address the research gap on algorithmic discrimination caused by AI-enabled recruitment and explore technical and managerial solutions. The primary research approach used is a literature review. The findings suggest that AI-enabled recruitment has the potential to enhance recruitment quality, increase efficiency, and reduce transactional work. However, algorithmic bias results in discriminatory hiring practices based on gender, race, color, and personality traits. The study indicates that algorithmic bias stems from limited raw data sets and biased algorithm designers. To mitigate this issue, it is recommended to implement technical measures, such as unbiased dataset frameworks and improved algorithmic transparency, as well as management measures like internal corporate ethical governance and external oversight. Employing Grounded Theory, the study conducted survey analysis to collect firsthand data on respondents’ experiences and perceptions of AI-driven recruitment applications and discrimination. + +## Scout Assessment + +- Relevance score: 8.0 +- Reason: Directly addresses AI recruitment efficiency, discriminatory outcomes, biased data and design, transparency, governance, and external oversight using review and survey evidence. +- Notes: Pending analyst notes. + +## Provenance + +Discovered via: query:artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection diff --git a/examples/ai-in-hiring-processes/reports/papers/evaluating-inclusivity-and-fairness-of-ai-recruitment-tools-for-hiring-people-with-disabil.md b/examples/ai-in-hiring-processes/reports/papers/evaluating-inclusivity-and-fairness-of-ai-recruitment-tools-for-hiring-people-with-disabil.md new file mode 100644 index 0000000..0d1906f --- /dev/null +++ b/examples/ai-in-hiring-processes/reports/papers/evaluating-inclusivity-and-fairness-of-ai-recruitment-tools-for-hiring-people-with-disabil.md @@ -0,0 +1,21 @@ +# Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE) + +- **Identifier:** `openalex:W4404228668` +- **Year:** 2024 +- **Primary category:** Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication +- **Source:** https://doi.org/10.56294/dm2025487 +- **Citations at discovery:** 110 + +## Abstract + +IntroductionThis research aims to explore the effectiveness and inclusivity of AI-powered recruitment tools in hiring people with disabilities within the United Arab Emirates. Such is the situation where AI integration into the arena of recruitment is increasingly rapid, while there are vital issues on the side of bias, accessibility, and fairness for applicants of diverse needs. MethodsThis study was a mixed-methods approach, examining sentiment analysis, emotion detection, and HR analytics of feedback from applicants with a disability, 415 in total. The research focused on scores referring to sentiment, the progression rate, and the outcome of the final hiring. ResultsThe sentiment score varied significantly across disability types (p-value <0.05). The applicants with cognitive disability expressed the highest sentiment sore while applicants with hearing impairment had the lowest, which indicated the varying adaptability of AI. The emotion analysis depicted a mix of positive and negative emotions. A few applicants liked technology and have trust in it, while others report fear. Clearly, the applicants, both disabled and non-disabled did not differ in their rate of progression (p-value >0.05), hence never indicating any significant difference within the initial steps of the process. The final hiring stage showed significant differences in results with (p-value <0.05), where the proportionate number of disabled applicants was recorded to be lower than that of non-disabled applicants + +## Scout Assessment + +- Relevance score: 9.0 +- Reason: Direct mixed-methods evaluation of accessibility, fairness, applicant experience, progression, and hiring outcomes for disabled applicants using AI recruitment tools in the UAE. +- Notes: Pending analyst notes. + +## Provenance + +Discovered via: query:algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability diff --git a/examples/ai-in-hiring-processes/reports/papers/fairness-ai-amp-recruitment.md b/examples/ai-in-hiring-processes/reports/papers/fairness-ai-amp-recruitment.md new file mode 100644 index 0000000..5f86edd --- /dev/null +++ b/examples/ai-in-hiring-processes/reports/papers/fairness-ai-amp-recruitment.md @@ -0,0 +1,21 @@ +# Fairness, AI & recruitment + +- **Identifier:** `openalex:W4394590365` +- **Year:** 2024 +- **Primary category:** Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security +- **Source:** https://doi.org/10.1016/j.clsr.2024.105966 +- **Citations at discovery:** 77 + +## Abstract + +The ever-increasing adoption of AI technologies in the hiring landscape to enhance human resources efficiency raises questions about algorithmic decision-making's implications in employment, especially for job applicants, including those at higher risk of social discrimination. Among other concepts, such as transparency and accountability, fairness has become crucial in AI recruitment debates due to the potential reproduction of bias and discrimination that can disproportionately affect certain vulnerable groups. However, the ideals and ambitions of fairness may signify different meanings to various stakeholders. Conceptualizing fairness is critical because it may provide a clear benchmark for evaluating and mitigating biases, ensuring that AI systems do not perpetuate existing imbalances and promote, in this case, equitable opportunities for all candidates in the job market. To this end, in this article, we conduct a scoping literature review on fairness in AI applications for recruitment and selection purposes, with special emphasis on its definition, categorization, and practical implementation. We start by explaining how AI applications have been increasingly used in the hiring process, especially to increase the efficiency of the HR team. We then move to the limitations of this technological innovation, which is known to be at high risk of privacy violations and social discrimination. Against this backdrop, we focus on defining and operationalizing fairness in AI applications for recruitment and selection purposes through cross-disciplinary lenses. Although the applicable legal frameworks and some research currently address the issue piecemeal, we observe and welcome the emergence of some cross-disciplinary efforts aimed at tackling this multifaceted challenge. We conclude the article with some brief recommendations to guide and shape future research and action on the fairness of AI applications in the hiring process for the better. + +## Scout Assessment + +- Relevance score: 9.0 +- Reason: Direct scoping review of fairness definitions and implementation in AI recruitment and selection, including bias, vulnerable groups, privacy, transparency, accountability, and legal frameworks. +- Notes: Pending analyst notes. + +## Provenance + +Discovered via: query:algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability diff --git a/examples/ai-in-hiring-processes/reports/papers/formal-proof-search.md b/examples/ai-in-hiring-processes/reports/papers/formal-proof-search.md new file mode 100644 index 0000000..ad0670c --- /dev/null +++ b/examples/ai-in-hiring-processes/reports/papers/formal-proof-search.md @@ -0,0 +1,21 @@ +# Formal proof search + +- **Identifier:** `openalex:1` +- **Year:** 2025 +- **Primary category:** Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication +- **Source:** https://example.com +- **Citations at discovery:** 10 + +## Abstract + +Proof search with verifier feedback. + +## Scout Assessment + +- Relevance score: 8.0 +- Reason: aligned +- Notes: Pending analyst notes. + +## Provenance + +Discovered via: query:q diff --git a/examples/ai-in-hiring-processes/reports/research_report.md b/examples/ai-in-hiring-processes/reports/research_report.md new file mode 100644 index 0000000..fadcb54 --- /dev/null +++ b/examples/ai-in-hiring-processes/reports/research_report.md @@ -0,0 +1,97 @@ +# AI in Hiring Processes: Research Scout Report + +**Goal:** Identify where AI can create measurable hiring value, where it introduces operational or legal risk, and which evidence supports responsible adoption and evaluation. + +**Accepted papers:** 8 + +**Scout runs:** 4 + +## Corpus Shape + +- **Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication:** 4 +- **AI function: generation, prediction, ranking, classification, recommendation, conversation, workflow automation:** 0 +- **Decision role: assistive, advisory, semi-automated, autonomous:** 0 +- **Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security:** 1 +- **Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor:** 2 +- **Risk mechanism: biased data, proxy variables, measurement error, distribution shift, gaming, hallucination, automation bias, weak oversight:** 0 +- **Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance:** 1 + +## Scout Usage + +- **2026-06-19:** 540 candidates, 0 accepted, 0 tokens, $0.00 USD via openalex (n/a) + Note: OpenAlex discovery does not invoke a model; token usage is zero. +- **2026-06-19:** 540 candidates, 0 accepted, 40597 tokens, $0.00 USD via codex (codex-cli:configured-model) + Note: Codex CLI reports token usage but does not expose billable USD cost in this workflow. +- **2026-06-20:** 1 candidates, 1 accepted, 0 tokens, $0.00 USD via openalex (n/a) + Note: OpenAlex discovery does not invoke a model; token usage is zero. +- **2026-06-20:** 540 candidates, 7 accepted, 40624 tokens, $0.00 USD via codex (codex-cli:configured-model) + Note: Codex CLI reports token usage but does not expose billable USD cost in this workflow. + +**Total scout tokens:** 81221 +**Total scout spend:** $0.00 + +## Papers + +### Comparing chatbots to psychometric tests in hiring: reduced social desirability bias, but lower predictive validity + +- Source: https://doi.org/10.3389/fpsyg.2025.1564979 +- Year: 2025 +- Category: Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication + +This paper explores the efficacy of AI-driven chatbots in accurately inferring personality traits compared to traditional psychometric tests within a real-world professional hiring context. The study is driven by the increasing integration of AI tools in recruitment processes, which necessitates a deeper understanding of their reliability and validity. Using a quasi-experimental design with propensity score matching, we analysed data from 159 candidates and other professionals from Serbian and Montenegrin regions who completed both traditional psychometric assessments and AI-based personality evaluations based on the Big Five Personality model. A novel one-question-per-facet approach was employed in the chatbot assessments with a goal of enabling more granular analysis of the chatbot's psychometric properties. The findings indicate that the chatbot demonstrated good structural, substantive, and convergent validity for certain traits, particularly Extraversion and Conscientiousness, but not for Neuroticism, Agreeableness, and Openness. While robust regression confirmed that AI-inferred scores are less susceptible to social desirability bias than traditional tests, they did not significantly predict real-world outcomes, indicating issues with external validity, particularly predictive validity. The results suggest that AI-driven chatbots show promise for identifying certain personality traits and demonstrate resistance to social desirability bias. This paper contributes to the emerging field of AI and psychometrics by offering insights into the potential and limitations of AI tools in professional selection, while developing an approach for refining psychometric properties of AI-driven assessments. + +### Formal proof search + +- Source: https://example.com +- Year: 2025 +- Category: Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication + +Proof search with verifier feedback. + +### A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring + +- Source: https://doi.org/10.3390/ai5010019 +- Year: 2024 +- Category: Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication + +The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms’ fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society. + +### A UTAUT-Based Framework for Analyzing Users’ Intention to Adopt Artificial Intelligence in Human Resource Recruitment: A Case Study of Thailand + +- Source: https://doi.org/10.3390/systems12010028 +- Year: 2024 +- Category: Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance + +Recruitment is a fundamental aspect of Human Resource Management to drive organizational performance. Traditional recruitment processes, with manual stages, are time-consuming and inefficient. Artificial Intelligence (AI), which demonstrates its potential in various sectors such as healthcare, education, and notable cases of ChatGPT, is currently reshaping recruitment by automating tasks to improve efficiency. However, in Thailand, where there is a growing demand for talents, the application of AI in recruitment remains relatively limited. This study focuses on human resources (HR) and recruitment professionals in Thailand, aiming to understand their perspectives on the integration of AI in recruitment. It utilized the Unified Theory for Acceptance and Use of Technology (UTAUT) model, customized to suit the specific requirements of Thailand recruitment practices. The study explores the factors influencing users’ intention to adopt AI in recruitment. Survey questionnaire items were created based on prior literature and refined with insights from HR and recruitment experts to ensure applicability in the context of recruitment in Thailand. A survey involving 364 HR and recruiting professionals in the Bangkok metropolitan area supplied comprehensive responses. The study reveals that several factors, including perceived value, perceived autonomy, effort expectancy, and facilitating conditions, significantly impact the intention to adopt AI for recruitment. While social influence and trust in AI technology do not have a direct influence on intention, social influence directly affects perceived value. Trust in AI technology positively influences Effort Expectancy. This study provides valuable benefits for HR and recruitment professionals, organizations, and AI developers by offering insights into AI adoption and sustainability, enhancing recruitment processes and promoting the effective use of AI tools in this sector. + +### Evaluating Inclusivity and Fairness of AI Recruitment Tools for Hiring People with Disabilities in the United Arab Emirates(UAE) + +- Source: https://doi.org/10.56294/dm2025487 +- Year: 2024 +- Category: Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication + +IntroductionThis research aims to explore the effectiveness and inclusivity of AI-powered recruitment tools in hiring people with disabilities within the United Arab Emirates. Such is the situation where AI integration into the arena of recruitment is increasingly rapid, while there are vital issues on the side of bias, accessibility, and fairness for applicants of diverse needs. MethodsThis study was a mixed-methods approach, examining sentiment analysis, emotion detection, and HR analytics of feedback from applicants with a disability, 415 in total. The research focused on scores referring to sentiment, the progression rate, and the outcome of the final hiring. ResultsThe sentiment score varied significantly across disability types (p-value <0.05). The applicants with cognitive disability expressed the highest sentiment sore while applicants with hearing impairment had the lowest, which indicated the varying adaptability of AI. The emotion analysis depicted a mix of positive and negative emotions. A few applicants liked technology and have trust in it, while others report fear. Clearly, the applicants, both disabled and non-disabled did not differ in their rate of progression (p-value >0.05), hence never indicating any significant difference within the initial steps of the process. The final hiring stage showed significant differences in results with (p-value <0.05), where the proportionate number of disabled applicants was recorded to be lower than that of non-disabled applicants + +### Fairness, AI & recruitment + +- Source: https://doi.org/10.1016/j.clsr.2024.105966 +- Year: 2024 +- Category: Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security + +The ever-increasing adoption of AI technologies in the hiring landscape to enhance human resources efficiency raises questions about algorithmic decision-making's implications in employment, especially for job applicants, including those at higher risk of social discrimination. Among other concepts, such as transparency and accountability, fairness has become crucial in AI recruitment debates due to the potential reproduction of bias and discrimination that can disproportionately affect certain vulnerable groups. However, the ideals and ambitions of fairness may signify different meanings to various stakeholders. Conceptualizing fairness is critical because it may provide a clear benchmark for evaluating and mitigating biases, ensuring that AI systems do not perpetuate existing imbalances and promote, in this case, equitable opportunities for all candidates in the job market. To this end, in this article, we conduct a scoping literature review on fairness in AI applications for recruitment and selection purposes, with special emphasis on its definition, categorization, and practical implementation. We start by explaining how AI applications have been increasingly used in the hiring process, especially to increase the efficiency of the HR team. We then move to the limitations of this technological innovation, which is known to be at high risk of privacy violations and social discrimination. Against this backdrop, we focus on defining and operationalizing fairness in AI applications for recruitment and selection purposes through cross-disciplinary lenses. Although the applicable legal frameworks and some research currently address the issue piecemeal, we observe and welcome the emergence of some cross-disciplinary efforts aimed at tackling this multifaceted challenge. We conclude the article with some brief recommendations to guide and shape future research and action on the fairness of AI applications in the hiring process for the better. + +### Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring + +- Source: https://doi.org/10.1111/1748-8583.12511 +- Year: 2023 +- Category: Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor + +Abstract Despite frequent claims that increased use of artificial intelligence (AI) in hiring will reduce the human bias that has long plagued recruitment and selection, AI may equally replicate and amplify such bias and embed it in technology. This article explores exclusion and inclusion in AI‐supported hiring, focusing on three interrelated areas: data, design and decisions. It is suggested that in terms of data, organisational fit, categorisations and intersectionality require consideration in relation to exclusion. As various stakeholders collaborate to create AI, it is essential to explore which groups are dominant and how subjective assessments are encoded in technology. Although AI‐supported hiring should enhance recruitment decisions, evidence is lacking on how humans and machines interact in decision‐making, and how algorithms can be audited and regulated effectively for inclusion. This article recommends areas for interrogation through further research, and contributes to understanding how algorithmic inclusion can be achieved in AI‐supported hiring. + +### Ethics and discrimination in artificial intelligence-enabled recruitment practices + +- Source: https://doi.org/10.1057/s41599-023-02079-x +- Year: 2023 +- Category: Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor + +Abstract This study aims to address the research gap on algorithmic discrimination caused by AI-enabled recruitment and explore technical and managerial solutions. The primary research approach used is a literature review. The findings suggest that AI-enabled recruitment has the potential to enhance recruitment quality, increase efficiency, and reduce transactional work. However, algorithmic bias results in discriminatory hiring practices based on gender, race, color, and personality traits. The study indicates that algorithmic bias stems from limited raw data sets and biased algorithm designers. To mitigate this issue, it is recommended to implement technical measures, such as unbiased dataset frameworks and improved algorithmic transparency, as well as management measures like internal corporate ethical governance and external oversight. Employing Grounded Theory, the study conducted survey analysis to collect firsthand data on respondents’ experiences and perceptions of AI-driven recruitment applications and discrimination. diff --git a/examples/ai-in-hiring-processes/scout_cron_payload.txt b/examples/ai-in-hiring-processes/scout_cron_payload.txt new file mode 100644 index 0000000..82450dd --- /dev/null +++ b/examples/ai-in-hiring-processes/scout_cron_payload.txt @@ -0,0 +1,28 @@ +AI Topic Scout scheduled run for: AI in Hiring Processes + +1. Read `AGENTS.md`, `topic.json`, and `skills/topic-paper-scout/SKILL.md`. +2. Run the configured searches and scholarly graph expansion. +3. Compare candidates against `data/papers.json`. +4. Apply inclusion rules: AI applications across the end-to-end hiring lifecycle, Decision-support, automation, and human-in-the-loop hiring systems, Impacts on hiring speed, cost, quality, consistency, and candidate experience, Validity, reliability, fairness, bias, accessibility, privacy, transparency, and explainability, Performance differences across roles, industries, applicant groups, and labor-market contexts, Data, workflow, monitoring, escalation, audit, and accountability requirements, Applicable employment, anti-discrimination, data-protection, and automated-decision constraints, Failure modes including proxy discrimination, feedback loops, gaming, automation bias, and model drift. +5. Apply exclusions: AI impacts on general workforce management after hiring, Employee productivity, retention, or performance-management systems unless directly used to validate hiring outcomes, Unsupported vendor marketing claims, Fully autonomous hiring recommendations without evaluation of human oversight and governance, Claims about legal compliance that are not tied to a defined jurisdiction. +6. Verify identifiers, source URLs, publication years, and abstracts. +7. If approval is required, write candidates and reviewer recommendations only. +8. If new papers are accepted: + - update `data/papers.json`; + - run `make corpus`; + - use `skills/analyze-research-gaps/SKILL.md`; + - validate the opportunity JSON; + - run `make dashboard`; + - commit and report only the accepted additions. +9. If no paper is accepted, do not rewrite tracked artifacts and return no update. +10. Include token_count and money_cost_usd in every scouting report and acceptance record. + +Topic-specific strategy: + - Map evidence to each hiring stage before comparing impacts across the full process. + - Separate technical benchmark performance from real-world organizational outcomes and candidate impacts. + - Prioritize studies with defined tasks, populations, baselines, subgroup analyses, and reproducible evaluation methods. + - Compare AI-only, human-only, and human-AI workflows where evidence permits. + - Extract business-value measures separately from validity, fairness, privacy, and candidate-experience measures. + - Record jurisdiction, job type, labor-market setting, deployment scale, and vendor dependence for each system. + - Assess agent-system requirements including tool permissions, data boundaries, human approval points, audit logs, escalation paths, and continuous monitoring. + - Search for both intended benefits and adversarial or failure-oriented evidence, including applicant gaming and recruiter automation bias. diff --git a/examples/ai-in-hiring-processes/skills/analyze-research-gaps/SKILL.md b/examples/ai-in-hiring-processes/skills/analyze-research-gaps/SKILL.md new file mode 100644 index 0000000..5433358 --- /dev/null +++ b/examples/ai-in-hiring-processes/skills/analyze-research-gaps/SKILL.md @@ -0,0 +1,24 @@ +--- +name: analyze-research-gaps +description: Analyze the AI in Hiring Processes corpus and write ranked, evidence-backed research opportunities. Use after accepted scout updates or when opportunity analysis is stale. +--- + +# Analyze Research Gaps + +1. Read `topic.json`, `data/papers.json`, `reports/research_report.md`, and `data/dashboard.json`. +2. Assess coverage scarcity, graph structure, evidence maturity, contradictions, and decision importance. +3. Separate `corpus_gap`, `field_gap`, `evidence_gap`, and `translation_gap`. +4. Rank 3-6 opportunities. +5. For each opportunity include: + - title, type, priority 0-100, and confidence; + - observed evidence and source paper IDs; + - explicit LLM inference; + - uncertainty and plausible counterevidence; + - at least two evidence observations with source paper IDs or artifact paths; + - a reproducible local coverage check with query terms and matched titles; + - at least two falsifiable research questions; + - at least two future scout queries. +6. Write `data/research_opportunities.json` using `schemas/research_opportunities.schema.json`. +7. Never infer field-wide absence from this corpus alone. +8. Do not claim completion until you have run the relevant generator or validator yourself and + verified that the written opportunity artifact is structurally valid and substantively usable. diff --git a/examples/ai-in-hiring-processes/skills/setup-topic-scout/SKILL.md b/examples/ai-in-hiring-processes/skills/setup-topic-scout/SKILL.md new file mode 100644 index 0000000..9575f6a --- /dev/null +++ b/examples/ai-in-hiring-processes/skills/setup-topic-scout/SKILL.md @@ -0,0 +1,42 @@ +--- +name: setup-topic-scout +description: Interview a user about a research topic and initialize AI Topic Scout. Use when topic.json is missing, when starting a new research workspace, or when the user wants generated agent roles, scout skills, queries, taxonomy, and inclusion/exclusion rules. +--- + +# Setup Topic Scout + +At the beginning of setup, collect the user's raw intent and initialize the LLM refinement step. +Do not use a sentence-length intent directly as the topic title. + +Ask the user for one complete intent that covers, where known: + +1. topic or research question; +2. business decision or outcome; +3. audience and domain or policy constraints; +4. evaluation concerns and agent-system design concerns. + +Then run `python3 scripts/init_topic.py --intent "..."` with explicit flags or interactively. +Pass additional known answers as optional constraints; do not delay refinement to conduct a full +manual taxonomy interview. +Normal setup uses `codex exec` and the Codex CLI's saved ChatGPT subscription login. If Codex is +not authenticated, run `codex login`. Use `--provider api` only when direct API usage is requested, +and use `--offline` only when deterministic initialization without LLM refinement is requested. + +The refined contract must contain: + +- a concise title and explicit research question; +- business purpose, domain constraints, and policy requirements; +- evaluation dimensions and agent-system design concerns; +- dashboard sections, taxonomy, and evidence types; +- direct, adversarial, and citation-graph scouting queries and strategy. + +Verify that initialization creates: + +- `topic.json` +- `AGENTS.md` +- `agents/*.md` +- `skills/topic-paper-scout/SKILL.md` +- `skills/analyze-research-gaps/SKILL.md` +- `data/papers.json` + +Do not start broad scouting until the user confirms the generated topic contract. diff --git a/examples/ai-in-hiring-processes/skills/topic-paper-scout/SKILL.md b/examples/ai-in-hiring-processes/skills/topic-paper-scout/SKILL.md new file mode 100644 index 0000000..16c28cd --- /dev/null +++ b/examples/ai-in-hiring-processes/skills/topic-paper-scout/SKILL.md @@ -0,0 +1,31 @@ +--- +name: topic-paper-scout +description: Scout, review, and ingest papers for AI in Hiring Processes. Use for scheduled scouting, manual paper discovery, citation-graph expansion, and corpus updates in this workspace. +--- + +# Topic Paper Scout + +1. Read `topic.json`, `data/papers.json`, and `AGENTS.md`. +2. Run `python3 scripts/scout.py`. +3. Review `data/candidates.json` against inclusion and exclusion rules. +4. Verify every accepted paper's identifier, URL, year, and abstract. +5. Add only genuinely new accepted papers to `data/papers.json`. +6. Run `make corpus`, then use `$analyze-research-gaps`, then run `make dashboard`. +7. If no paper is accepted, do not rewrite corpus, report, dashboard, or opportunity artifacts. +8. Do not stop at implementation alone. Run the code paths you changed, verify the generated files + or outputs yourself, and only report success after the executed results are good. + +Search within 2023-2026. +Prioritize: methods, benchmarks, systems, surveys. +Report token_count and money_cost_usd for every scouting run, even when the cost is zero. + +## Topic-Specific Scouting Strategy + +- Map evidence to each hiring stage before comparing impacts across the full process. +- Separate technical benchmark performance from real-world organizational outcomes and candidate impacts. +- Prioritize studies with defined tasks, populations, baselines, subgroup analyses, and reproducible evaluation methods. +- Compare AI-only, human-only, and human-AI workflows where evidence permits. +- Extract business-value measures separately from validity, fairness, privacy, and candidate-experience measures. +- Record jurisdiction, job type, labor-market setting, deployment scale, and vendor dependence for each system. +- Assess agent-system requirements including tool permissions, data boundaries, human approval points, audit logs, escalation paths, and continuous monitoring. +- Search for both intended benefits and adversarial or failure-oriented evidence, including applicant gaming and recruiter automation bias. diff --git a/examples/ai-in-hiring-processes/topic-dashboard.html b/examples/ai-in-hiring-processes/topic-dashboard.html new file mode 100644 index 0000000..dab9d9d --- /dev/null +++ b/examples/ai-in-hiring-processes/topic-dashboard.html @@ -0,0 +1,65 @@ + + +AI in Hiring Processes · AI Topic Scout +
+
AI Topic Scout · Living research intelligence

+
+
LLM-refined research contract

What this scout evaluates

Dashboard structure

    Discovery design

    Scouting strategy

      +
      Corpus distribution

      Research categories by visualized papers

      +
      Scout history

      Category share over time

      +
      Candidate queue

      Discovered papers awaiting review

      +
      Compiled knowledge base

      Research Wiki

      +
      Association map

      Paper Graph

      +
      LLM reasoning

      Underexplored areas & opportunities

      +
      Evidence ledger

      Accepted papers

      YearPaperCategoryCitations
      +
      \ No newline at end of file diff --git a/examples/ai-in-hiring-processes/topic.json b/examples/ai-in-hiring-processes/topic.json new file mode 100644 index 0000000..ba8ee26 --- /dev/null +++ b/examples/ai-in-hiring-processes/topic.json @@ -0,0 +1,83 @@ +{ + "slug": "ai-in-hiring-processes", + "topic": "AI in Hiring Processes", + "raw_intent": "How AI could impact on the hiring process?", + "research_question": "How does AI affect the design, execution, outcomes, and governance of hiring processes across sourcing, screening, assessment, interviewing, selection, and candidate communication?", + "goal": "Identify where AI can create measurable hiring value, where it introduces operational or legal risk, and which evidence supports responsible adoption and evaluation.", + "audience": "Talent acquisition leaders, HR operations teams, hiring managers, AI system designers, legal and compliance teams, and organizational decision-makers.", + "include": [ + "AI applications across the end-to-end hiring lifecycle", + "Decision-support, automation, and human-in-the-loop hiring systems", + "Impacts on hiring speed, cost, quality, consistency, and candidate experience", + "Validity, reliability, fairness, bias, accessibility, privacy, transparency, and explainability", + "Performance differences across roles, industries, applicant groups, and labor-market contexts", + "Data, workflow, monitoring, escalation, audit, and accountability requirements", + "Applicable employment, anti-discrimination, data-protection, and automated-decision constraints", + "Failure modes including proxy discrimination, feedback loops, gaming, automation bias, and model drift" + ], + "exclude": [ + "AI impacts on general workforce management after hiring", + "Employee productivity, retention, or performance-management systems unless directly used to validate hiring outcomes", + "Unsupported vendor marketing claims", + "Fully autonomous hiring recommendations without evaluation of human oversight and governance", + "Claims about legal compliance that are not tied to a defined jurisdiction" + ], + "years": { + "from": 2023, + "to": 2026 + }, + "evidence_types": [ + "methods", + "benchmarks", + "systems", + "surveys" + ], + "taxonomy": [ + "Hiring stage: sourcing, matching, screening, assessment, interviewing, selection, communication", + "AI function: generation, prediction, ranking, classification, recommendation, conversation, workflow automation", + "Decision role: assistive, advisory, semi-automated, autonomous", + "Evaluation dimension: utility, validity, reliability, efficiency, fairness, privacy, transparency, accessibility, security", + "Stakeholder: applicant, recruiter, hiring manager, employer, vendor, regulator, auditor", + "Risk mechanism: biased data, proxy variables, measurement error, distribution shift, gaming, hallucination, automation bias, weak oversight", + "Governance control: documentation, consent, human review, appeal, audit, monitoring, incident response, vendor assurance" + ], + "dashboard_sections": [ + "AI use cases by hiring stage and decision role", + "Business-value metrics: time-to-hire, cost-per-hire, recruiter workload, conversion, and quality-of-hire", + "Evidence quality and benchmark comparability", + "Predictive validity, reliability, and generalization across jobs and populations", + "Fairness outcomes and adverse-impact analysis by applicant group", + "Candidate experience, accessibility, transparency, and contestability", + "Human-AI workflow design, override behavior, and accountability", + "Legal and policy requirements by jurisdiction", + "Privacy, security, data provenance, and vendor-risk controls", + "Adversarial risks, failure modes, monitoring signals, and mitigations" + ], + "cadence": "weekly", + "approval_required": false, + "search_queries": [ + "artificial intelligence impact hiring recruitment sourcing screening assessment interviewing selection", + "AI hiring benchmark candidate screening job matching interview assessment predictive validity reliability", + "AI recruitment system human in the loop recruiter decision support workflow architecture monitoring", + "algorithmic hiring employment discrimination automated decision systems law regulation audit privacy", + "AI recruiting business value time to hire cost per hire quality of hire recruiter productivity ROI", + "algorithmic hiring bias fairness adverse impact accessibility candidate experience explainability", + "AI hiring adversarial gaming prompt injection resume manipulation deepfake interview fraud automation bias model drift", + "generative AI recruiting systems candidate communication job descriptions interview assistants", + "algorithmic hiring survey employers recruiters applicants adoption trust attitudes", + "AI hiring methods causal evaluation field experiment longitudinal quality of hire" + ], + "scouting_strategy": [ + "Map evidence to each hiring stage before comparing impacts across the full process.", + "Separate technical benchmark performance from real-world organizational outcomes and candidate impacts.", + "Prioritize studies with defined tasks, populations, baselines, subgroup analyses, and reproducible evaluation methods.", + "Compare AI-only, human-only, and human-AI workflows where evidence permits.", + "Extract business-value measures separately from validity, fairness, privacy, and candidate-experience measures.", + "Record jurisdiction, job type, labor-market setting, deployment scale, and vendor dependence for each system.", + "Assess agent-system requirements including tool permissions, data boundaries, human approval points, audit logs, escalation paths, and continuous monitoring.", + "Search for both intended benefits and adversarial or failure-oriented evidence, including applicant gaming and recruiter automation bias." + ], + "intent_refinement_provider": "codex", + "intent_refinement_model": "codex-cli:configured-model", + "created_at": "2026-06-19" +} diff --git a/scripts/accept_candidates.py b/scripts/accept_candidates.py index 228c085..d677422 100644 --- a/scripts/accept_candidates.py +++ b/scripts/accept_candidates.py @@ -6,6 +6,7 @@ import argparse from datetime import datetime, timezone +from costs import zero_cost from workspace import CANDIDATES_PATH, PAPERS_PATH, load_json, load_topic, write_json @@ -45,13 +46,26 @@ def main() -> int: if not accepted: print("No new papers accepted; corpus unchanged.") return 0 - corpus["scout_runs"].append( - { - "date": timestamp[:10], - "queries": candidates.get("queries", []), - "accepted_ids": accepted, - } - ) + scout_runs = corpus.setdefault("scout_runs", []) + if scout_runs and scout_runs[-1].get("accepted_ids", []) == []: + scout_runs[-1].update( + { + "accepted_ids": accepted, + "accepted_count": len(accepted), + "cost": candidates.get("cost", zero_cost()), + } + ) + else: + scout_runs.append( + { + "date": timestamp[:10], + "queries": candidates.get("queries", []), + "accepted_ids": accepted, + "accepted_count": len(accepted), + "candidate_count": len(candidates.get("candidates", [])), + "cost": candidates.get("cost", zero_cost()), + } + ) write_json(PAPERS_PATH, corpus) print(f"Accepted {len(accepted)} papers. Run `make corpus`.") return 0 diff --git a/scripts/analyze_research_gaps.py b/scripts/analyze_research_gaps.py new file mode 100644 index 0000000..c3860ea --- /dev/null +++ b/scripts/analyze_research_gaps.py @@ -0,0 +1,294 @@ +#!/usr/bin/env python3 +"""Generate evidence-backed research opportunities from the accepted corpus.""" + +from __future__ import annotations + +import argparse +import json +import os +import shutil +import subprocess +import tempfile +import urllib.error +import urllib.request +from datetime import datetime, timezone +from pathlib import Path + +from intent_refiner import DEFAULT_MODEL, RESPONSES_URL, _output_text +from workspace import OPPORTUNITIES_PATH, PAPERS_PATH, REPORT_PATH, TOPIC_CONFIG, load_json, write_json + + +OPPORTUNITY_SCHEMA = { + "type": "object", + "properties": { + "summary": {"type": "string", "minLength": 20}, + "opportunities": { + "type": "array", + "minItems": 3, + "maxItems": 6, + "items": { + "type": "object", + "properties": { + "rank": {"type": "integer", "minimum": 1, "maximum": 6}, + "title": {"type": "string", "minLength": 5}, + "gap_type": { + "type": "string", + "enum": ["corpus_gap", "field_gap", "evidence_gap", "translation_gap"], + }, + "priority_score": {"type": "integer", "minimum": 0, "maximum": 100}, + "confidence": {"type": "string", "enum": ["low", "medium", "high"]}, + "scope": {"type": "string", "minLength": 10}, + "evidence": { + "type": "array", + "minItems": 2, + "items": { + "type": "object", + "properties": { + "observation": {"type": "string", "minLength": 5}, + "source": {"type": "string", "minLength": 3}, + }, + "required": ["observation", "source"], + "additionalProperties": False, + }, + }, + "llm_reasoning": {"type": "string", "minLength": 10}, + "uncertainty": {"type": "string", "minLength": 10}, + "coverage_check": { + "type": "object", + "properties": { + "query_terms": { + "type": "array", + "minItems": 1, + "items": {"type": "string", "minLength": 2}, + }, + "matched_titles": { + "type": "array", + "items": {"type": "string"}, + }, + "interpretation": {"type": "string", "minLength": 10}, + }, + "required": ["query_terms", "matched_titles", "interpretation"], + "additionalProperties": False, + }, + "research_questions": { + "type": "array", + "minItems": 2, + "items": {"type": "string", "minLength": 10}, + }, + "scout_queries": { + "type": "array", + "minItems": 2, + "items": {"type": "string", "minLength": 3}, + }, + }, + "required": [ + "rank", + "title", + "gap_type", + "priority_score", + "confidence", + "scope", + "evidence", + "llm_reasoning", + "uncertainty", + "coverage_check", + "research_questions", + "scout_queries", + ], + "additionalProperties": False, + }, + }, + }, + "required": ["summary", "opportunities"], + "additionalProperties": False, +} + +INSTRUCTIONS = ( + "You are the gap analyst for AI Topic Scout. Read the provided accepted-paper corpus, report " + "summary, and topic contract. Produce only bounded, evidence-backed research opportunities. " + "Do not infer field-wide absence from sparse corpus evidence. Each opportunity must cite " + "specific local evidence, explain the inference, note uncertainty, and propose follow-up " + "scout queries." +) + + +class OpportunityAnalysisError(RuntimeError): + """Raised when gap analysis fails.""" + + +def _input_payload() -> str: + topic = json.loads(TOPIC_CONFIG.read_text(encoding="utf-8")) + corpus = load_json(PAPERS_PATH, {"papers": [], "scout_runs": []}) + report = REPORT_PATH.read_text(encoding="utf-8") if REPORT_PATH.exists() else "" + papers = corpus.get("papers", []) + compact_papers = [ + { + "id": paper["id"], + "title": paper.get("title", ""), + "year": paper.get("year"), + "primary_category": paper.get("primary_category"), + "citation_count": paper.get("citation_count", 0), + "topics": paper.get("topics", [])[:6], + "relevance_reason": paper.get("relevance_reason", ""), + "abstract": (paper.get("abstract") or "")[:1200], + } + for paper in papers[:80] + ] + scout_runs = corpus.get("scout_runs", [])[-8:] + return json.dumps( + { + "topic": { + "topic": topic.get("topic"), + "research_question": topic.get("research_question"), + "goal": topic.get("goal"), + "include": topic.get("include", []), + "exclude": topic.get("exclude", []), + "taxonomy": topic.get("taxonomy", []), + "search_queries": topic.get("search_queries", []), + "dashboard_sections": topic.get("dashboard_sections", []), + }, + "report_excerpt": report[:12000], + "accepted_paper_count": len(papers), + "papers": compact_papers, + "scout_runs": scout_runs, + }, + ensure_ascii=False, + ) + + +def _finalize(parsed: dict, model_name: str) -> dict: + payload = { + "generated_at": datetime.now(timezone.utc).isoformat(), + "analysis_model": model_name, + "summary": parsed["summary"].strip(), + "opportunities": parsed["opportunities"], + } + return payload + + +def analyze_api(*, api_key: str | None = None, model: str | None = None, urlopen=urllib.request.urlopen) -> tuple[dict, str]: + key = api_key or os.environ.get("OPENAI_API_KEY") + if not key: + raise OpportunityAnalysisError( + "OPENAI_API_KEY is required for research-gap analysis. Set it or use --provider codex." + ) + selected_model = model or os.environ.get("TOPIC_SCOUT_GAP_MODEL") or os.environ.get("TOPIC_SCOUT_MODEL", DEFAULT_MODEL) + request = urllib.request.Request( + RESPONSES_URL, + data=json.dumps( + { + "model": selected_model, + "store": False, + "instructions": INSTRUCTIONS, + "input": _input_payload(), + "reasoning": {"effort": "medium"}, + "text": { + "verbosity": "low", + "format": { + "type": "json_schema", + "name": "research_opportunities", + "strict": True, + "schema": OPPORTUNITY_SCHEMA, + }, + }, + } + ).encode("utf-8"), + headers={ + "Authorization": f"Bearer {key}", + "Content-Type": "application/json", + }, + method="POST", + ) + try: + with urlopen(request, timeout=180) as response: + body = json.loads(response.read().decode("utf-8")) + except urllib.error.HTTPError as exc: + detail = exc.read().decode("utf-8", errors="replace") + raise OpportunityAnalysisError(f"OpenAI gap analysis failed with HTTP {exc.code}: {detail}") from exc + except (urllib.error.URLError, TimeoutError, json.JSONDecodeError) as exc: + raise OpportunityAnalysisError(f"OpenAI gap analysis failed: {exc}") from exc + try: + parsed = json.loads(_output_text(body)) + except json.JSONDecodeError as exc: + raise OpportunityAnalysisError("OpenAI gap analysis returned invalid structured JSON") from exc + return _finalize(parsed, selected_model), selected_model + + +def analyze_codex(*, model: str | None = None, cwd: Path | str | None = None, run=subprocess.run) -> tuple[dict, str]: + executable = shutil.which("codex") + if not executable: + raise OpportunityAnalysisError("Codex CLI is not installed. Install it, use --provider api, or skip opportunities generation.") + prompt = ( + f"{INSTRUCTIONS}\n\n" + "Return only the requested structured output. Do not inspect files, run commands, or modify the workspace.\n\n" + f"Input:\n{_input_payload()}" + ) + with tempfile.TemporaryDirectory(prefix="topic-scout-gaps-") as directory: + temp_dir = Path(directory) + schema_path = temp_dir / "schema.json" + output_path = temp_dir / "result.json" + schema_path.write_text(json.dumps(OPPORTUNITY_SCHEMA), encoding="utf-8") + command = [ + executable, + "exec", + "--ephemeral", + "--ignore-user-config", + "--ignore-rules", + "--sandbox", + "read-only", + "--color", + "never", + "--output-schema", + str(schema_path), + "--output-last-message", + str(output_path), + ] + if model: + command.extend(["--model", model]) + command.append(prompt) + try: + result = run( + command, + cwd=str(cwd) if cwd else None, + stdin=subprocess.DEVNULL, + capture_output=True, + text=True, + timeout=240, + ) + except (OSError, subprocess.TimeoutExpired) as exc: + raise OpportunityAnalysisError(f"Codex gap analysis failed: {exc}") from exc + if result.returncode != 0: + detail = (result.stderr or result.stdout).strip() + raise OpportunityAnalysisError(f"Codex gap analysis failed: {detail or 'unknown error'}") + output = output_path.read_text(encoding="utf-8") if output_path.exists() else result.stdout + try: + parsed = json.loads(output) + except json.JSONDecodeError as exc: + raise OpportunityAnalysisError("Codex gap analysis returned invalid structured JSON") from exc + model_name = f"codex-cli:{model or 'configured-model'}" + return _finalize(parsed, model_name), model_name + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--provider", choices=["codex", "api"], default=os.environ.get("TOPIC_SCOUT_GAP_PROVIDER", "codex")) + parser.add_argument("--model", help="Model override for gap analysis") + args = parser.parse_args() + corpus = load_json(PAPERS_PATH, {"papers": []}) + if not corpus.get("papers"): + print("No accepted papers yet; skipping research opportunities.") + return 0 + try: + if args.provider == "api": + payload, model_name = analyze_api(model=args.model) + else: + payload, model_name = analyze_codex(model=args.model) + except OpportunityAnalysisError as exc: + raise SystemExit(f"Gap analysis failed: {exc}") + write_json(OPPORTUNITIES_PATH, payload) + print(f"Wrote {OPPORTUNITIES_PATH} with {len(payload['opportunities'])} opportunities via {model_name}.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/build_corpus.py b/scripts/build_corpus.py index 1b30d9a..765dbb2 100644 --- a/scripts/build_corpus.py +++ b/scripts/build_corpus.py @@ -13,6 +13,21 @@ def slug(value: str) -> str: return re.sub(r"[^a-z0-9]+", "-", value.lower()).strip("-")[:90] +def category_tokens(category: str) -> list[str]: + stop = { + "and", "or", "the", "of", "for", "in", "to", "by", "with", "on", "a", "an", + "that", "this", "their", "other", "across", "through", "while", "when", + "which", "from", "into", "under", "over", "using", "use", "used", + "technical", "documented", "relevant", "including", "should", "include", + "evidence", "context", "quality", "details", "analysis", "assessment", + } + return [ + token + for token in re.findall(r"[a-z0-9]+", category.lower()) + if len(token) >= 4 and token not in stop + ][:12] + + def classify(paper: dict, config: dict) -> str: if paper.get("primary_category") not in (None, "", "unclassified"): return paper["primary_category"] @@ -20,7 +35,7 @@ def classify(paper: dict, config: dict) -> str: [paper.get("title", ""), paper.get("abstract", ""), *paper.get("topics", [])] ).lower() scores = { - category: sum(token in text for token in category.lower().split()) + category: sum(token in text for token in category_tokens(category)) for category in config["taxonomy"] } return max(config["taxonomy"], key=lambda category: (scores[category], -config["taxonomy"].index(category))) @@ -30,6 +45,7 @@ def main() -> int: config = load_topic() corpus = load_json(PAPERS_PATH, {"papers": [], "scout_runs": []}) papers = corpus["papers"] + scout_runs = corpus.get("scout_runs", []) notes_dir = REPORTS_DIR / "papers" notes_dir.mkdir(parents=True, exist_ok=True) counts = Counter() @@ -68,10 +84,40 @@ def main() -> int: "", f"**Accepted papers:** {len(papers)}", "", + f"**Scout runs:** {len(scout_runs)}", + "", "## Corpus Shape", "", ] lines.extend(f"- **{category}:** {counts[category]}" for category in config["taxonomy"]) + total_tokens = 0 + total_cost = 0.0 + lines.extend(["", "## Scout Usage", ""]) + if scout_runs: + for run in scout_runs: + cost = run.get("cost") or {} + total_tokens += int(cost.get("token_count", 0) or 0) + total_cost += float(cost.get("money_cost_usd", 0.0) or 0.0) + provider = cost.get("provider", "unknown") + model = cost.get("model") or "n/a" + lines.append( + f"- **{run.get('date', 'unknown date')}:** {run.get('candidate_count', 0)} candidates, " + f"{run.get('accepted_count', len(run.get('accepted_ids', [])))} accepted, " + f"{int(cost.get('token_count', 0) or 0)} tokens, " + f"${float(cost.get('money_cost_usd', 0.0) or 0.0):.2f} " + f"{cost.get('currency', 'USD')} via {provider} ({model})" + ) + if cost.get("note"): + lines.append(f" Note: {cost['note']}") + else: + lines.append("- No scout runs recorded yet.") + lines.extend( + [ + "", + f"**Total scout tokens:** {total_tokens}", + f"**Total scout spend:** ${total_cost:.2f}", + ] + ) lines.extend(["", "## Papers", ""]) for paper in sorted(papers, key=lambda item: (-(item.get("year") or 0), item["title"])): lines.extend( diff --git a/scripts/build_dashboard.py b/scripts/build_dashboard.py index de1bd15..bfa81bc 100644 --- a/scripts/build_dashboard.py +++ b/scripts/build_dashboard.py @@ -11,6 +11,7 @@ from workspace import ( DASHBOARD_PATH, DATA_DIR, + CANDIDATES_PATH, OPPORTUNITIES_PATH, PAPERS_PATH, load_json, @@ -87,7 +88,7 @@ def graph_data(papers: list[dict]) -> dict: } -def wiki_data(config: dict, papers: list[dict], graph: dict) -> dict: +def wiki_data(config: dict, papers: list[dict], graph: dict, candidates: list[dict] | None = None) -> dict: nodes = {node["id"]: node for node in graph["nodes"]} related: dict[str, list[dict]] = defaultdict(list) for edge in graph["edges"]: @@ -104,12 +105,24 @@ def wiki_data(config: dict, papers: list[dict], graph: dict) -> dict: "title": config["topic"], "subtitle": config["goal"], "body": ( - f"This living wiki contains {len(papers)} accepted papers for {config['audience']}. " - "Browse category pages or follow related-paper trails." + f"This living wiki contains {len(papers)} accepted papers for {config['audience']} " + f"and {len(candidates or [])} candidate papers awaiting review. " + "Browse category pages, the candidate queue, or follow related-paper trails." ), "links": [f"category:{category}" for category in config["taxonomy"]], } ] + if candidates: + pages.append( + { + "id": "candidate-queue", + "type": "queue", + "title": "Candidate Queue", + "subtitle": f"{len(candidates)} papers awaiting review", + "body": "Top discovered papers that have not been accepted into the corpus yet.", + "links": [f"candidate:{paper['id']}" for paper in candidates], + } + ) for category in config["taxonomy"]: members = [paper for paper in papers if paper["primary_category"] == category] pages.append( @@ -136,32 +149,90 @@ def wiki_data(config: dict, papers: list[dict], graph: dict) -> dict: "links": [f"category:{paper['primary_category']}"], } ) + for candidate in candidates or []: + pages.append( + { + "id": f"candidate:{candidate['id']}", + "type": "candidate", + "title": candidate["title"], + "subtitle": f"pending review · {candidate.get('year') or 'year unknown'}", + "body": candidate.get("abstract") or "Abstract unavailable.", + "source_url": candidate.get("url"), + "assessment": candidate.get("relevance_reason"), + "links": ["candidate-queue"], + } + ) return {"pages": pages, "page_count": len(pages)} def build_data() -> dict: config = load_topic() corpus = load_json(PAPERS_PATH, {"papers": [], "scout_runs": []}) + candidates_payload = load_json(CANDIDATES_PATH, {"candidates": [], "generated_at": None, "cost": {}}) papers = corpus["papers"] for paper in papers: paper["primary_category"] = classify(paper, config) - counts = Counter(paper["primary_category"] for paper in papers) categories = [ { "id": category, "label": category.title(), - "count": counts[category], - "ratio": counts[category] / len(papers) if papers else 0, + "count": 0, + "ratio": 0, "color": COLORS[index % len(COLORS)], } for index, category in enumerate(config["taxonomy"]) ] runs = [] cumulative = 0 + cumulative_topics = {category["id"]: 0 for category in categories} + total_tokens = 0 + total_cost = 0.0 + paper_by_id = {paper["id"]: paper for paper in papers} for run in corpus.get("scout_runs", []): - cumulative += len(run.get("accepted_ids", [])) - runs.append({**run, "accepted": len(run.get("accepted_ids", [])), "cumulative": cumulative}) - graph = graph_data(papers) + accepted_count = int(run.get("accepted_count", len(run.get("accepted_ids", []))) or 0) + candidate_count = int(run.get("candidate_count", 0) or 0) + cumulative += accepted_count + cost = run.get("cost") or {} + total_tokens += int(cost.get("token_count", 0) or 0) + total_cost += float(cost.get("money_cost_usd", 0.0) or 0.0) + accepted_topic_counts = {category["id"]: 0 for category in categories} + for identifier in run.get("accepted_ids", []): + paper = paper_by_id.get(identifier) + if not paper: + continue + accepted_topic_counts[paper["primary_category"]] += 1 + for category_id, count in accepted_topic_counts.items(): + cumulative_topics[category_id] += count + runs.append( + { + **run, + "accepted": accepted_count, + "candidate_count": candidate_count, + "cumulative": cumulative, + "accepted_topic_counts": accepted_topic_counts, + "cumulative_topics": dict(cumulative_topics), + "cumulative_topic_ratios": { + category_id: (count / cumulative if cumulative else 0.0) + for category_id, count in cumulative_topics.items() + }, + "token_count": int(cost.get("token_count", 0) or 0), + "money_cost_usd": float(cost.get("money_cost_usd", 0.0) or 0.0), + "currency": cost.get("currency", "USD"), + } + ) + candidate_rows = sorted( + candidates_payload.get("candidates", []), + key=lambda item: (-item.get("relevance_score", 0), -item.get("citation_count", 0), -(item.get("year") or 0)), + ) + candidate_cost = candidates_payload.get("cost") or {} + visual_papers = papers if papers else [dict(item) for item in candidate_rows[:60]] + for paper in visual_papers: + paper["primary_category"] = classify(paper, config) + counts = Counter(paper["primary_category"] for paper in visual_papers) + for category in categories: + category["count"] = counts[category["id"]] + category["ratio"] = counts[category["id"]] / len(visual_papers) if visual_papers else 0 + graph = graph_data(visual_papers) opportunities = load_json( OPPORTUNITIES_PATH, { @@ -174,10 +245,28 @@ def build_data() -> dict: return { "topic": config, "paper_count": len(papers), + "visualized_paper_count": len(visual_papers), + "visualized_source": "accepted papers" if papers else "discovered candidates", "categories": categories, "runs": runs, + "scout_tokens": total_tokens, + "scout_spend_usd": round(total_cost, 2), + "candidate_count": len(candidate_rows), + "candidate_generated_at": candidates_payload.get("generated_at"), + "candidate_cost": { + "provider": candidate_cost.get("provider", candidates_payload.get("scout_provider", "unknown")), + "model": candidate_cost.get("model"), + "token_count": int(candidate_cost.get("token_count", 0) or 0), + "input_tokens": int(candidate_cost.get("input_tokens", 0) or 0), + "output_tokens": int(candidate_cost.get("output_tokens", 0) or 0), + "reasoning_tokens": int(candidate_cost.get("reasoning_tokens", 0) or 0), + "money_cost_usd": float(candidate_cost.get("money_cost_usd", 0.0) or 0.0), + "currency": candidate_cost.get("currency", "USD"), + "note": candidate_cost.get("note", ""), + }, + "candidates": candidate_rows[:25], "graph": graph, - "wiki": wiki_data(config, papers, graph), + "wiki": wiki_data(config, papers or visual_papers, graph, candidate_rows[:25]), "opportunities": opportunities, "papers": papers, } @@ -193,23 +282,27 @@ def build_data() -> dict: main{position:relative;width:min(1480px,calc(100% - 30px));margin:auto;padding:34px 0 70px} header{border-top:8px solid var(--ink);padding-top:18px}.eyebrow{text-transform:uppercase;letter-spacing:.15em;font-size:10px;font-weight:800} h1{font:700 clamp(42px,7vw,92px)/.9 Georgia,serif;letter-spacing:-.055em;margin:8px 0}.subtitle{max-width:850px;color:var(--muted);font-size:17px} -.metrics{display:grid;grid-template-columns:repeat(4,1fr);gap:1px;background:var(--line);border:1px solid var(--line);margin:26px 0}.metric{background:var(--panel);padding:17px}.metric b{display:block;font:700 35px Georgia,serif;margin-top:14px} +.metrics{display:grid;grid-template-columns:repeat(7,1fr);gap:1px;background:var(--line);border:1px solid var(--line);margin:26px 0}.metric{background:var(--panel);padding:17px}.metric b{display:block;font:700 35px Georgia,serif;margin-top:14px} .grid{display:grid;grid-template-columns:1fr 1.4fr;gap:17px}.panel{background:rgba(255,253,246,.94);border:1px solid var(--line);padding:20px;min-width:0}.wide{grid-column:1/-1}.panel h2{font:700 26px Georgia,serif;margin:5px 0 16px} .framing{display:grid;grid-template-columns:1.4fr 1fr;gap:24px}.framing ul{columns:2;padding-left:18px}.framing li{margin-bottom:7px} -.barrow{display:grid;grid-template-columns:1fr 2fr 48px;gap:9px;align-items:center;margin:12px 0}.bar{height:12px;background:#ded8ca}.bar i{display:block;height:100%;background:var(--c);width:var(--w)} +.barrow{display:grid;grid-template-columns:1fr 2fr 84px;gap:9px;align-items:center;margin:12px 0}.bar{height:12px;background:#ded8ca}.bar i{display:block;height:100%;background:var(--c);width:var(--w)} +.legend{display:flex;flex-wrap:wrap;gap:8px 14px;margin:4px 0 14px;font-size:12px}.legend span::before{content:"";display:inline-block;width:9px;height:9px;margin-right:6px;background:var(--c)} +svg{width:100%;height:auto;overflow:visible}.axis{stroke:#bcb4a4;stroke-width:1}.chart-label{fill:#706c63;font-size:11px}.trend-line{fill:none;stroke-width:3;vector-effect:non-scaling-stroke}.run-dot{stroke:var(--panel);stroke-width:2} .wiki{padding:0}.wikihead,.graphhead{display:flex;justify-content:space-between;align-items:end;gap:18px;padding:20px;border-bottom:1px solid var(--line)}input,select,button{font:inherit}.wikihead input,.graphhead input,.graphhead select{padding:9px;border:1px solid currentColor;background:transparent} .wikilayout{display:grid;grid-template-columns:270px 1fr;min-height:620px}.wikinav{background:#eee8dc;border-right:1px solid var(--line);max-height:700px;overflow:auto;padding:10px}.wikinav button{display:block;width:100%;padding:10px 7px;border:0;border-bottom:1px solid #d2cabb;background:transparent;text-align:left;cursor:pointer}.wikinav button.active,.wikinav button:hover{background:var(--panel)}.wikinav small{display:block;color:var(--muted);text-transform:uppercase} .wikipage{padding:clamp(22px,4vw,54px);max-height:700px;overflow:auto}.wikipage h3{font:700 clamp(30px,5vw,58px)/1 Georgia,serif;margin:7px 0}.wikipage .lede{font:italic 19px Georgia,serif;color:var(--muted)}.links{display:flex;flex-wrap:wrap;gap:7px}.links button,.related button{border:1px solid var(--line);background:#f7f3e9;padding:9px;text-align:left;cursor:pointer}.related{display:grid;grid-template-columns:repeat(2,1fr);gap:8px}.related small{display:block;color:var(--muted);margin-top:4px} -.graphpanel{padding:0;background:#17201f;color:#f3efe4}.graphlayout{display:grid;grid-template-columns:1fr 280px}.stage{position:relative;overflow:hidden;background:radial-gradient(circle,#263330,#111817)}canvas{display:block;width:100%;height:620px;touch-action:none}.detail{padding:18px;border-left:1px solid #44504e}.detail h3{font:700 23px Georgia,serif}.detail p{color:#bac4c1} +.graphpanel{padding:0;background:#17201f;color:#f3efe4}.graphlayout{display:grid;grid-template-columns:minmax(0,1fr) 220px}.stage{position:relative;overflow:hidden;background:radial-gradient(circle,#263330,#111817)}canvas{display:block;width:100%;height:360px;touch-action:none}.detail{padding:16px;border-left:1px solid #44504e}.detail h3{font:700 23px Georgia,serif}.detail p{color:#bac4c1} .opportunity{display:grid;grid-template-columns:65px 1fr 220px;gap:16px;padding:17px 0;border-bottom:1px solid var(--line)}.rank{font:700 38px Georgia,serif;color:#e4572e}.score{font:700 31px Georgia,serif}.tag{display:inline-block;border:1px solid;padding:3px 6px;font-size:9px;text-transform:uppercase;margin-right:5px} +.candidate{display:grid;grid-template-columns:65px 1fr 220px;gap:16px;padding:17px 0;border-bottom:1px solid var(--line)}.candidate .rank{font:700 38px Georgia,serif;color:#1d6f75}.candidate .meta{font-size:12px;color:var(--muted);margin-top:6px}.candidate h3{margin:0 0 6px;font:700 22px Georgia,serif}.candidate p{margin:4px 0} table{border-collapse:collapse;width:100%;font-size:13px}th,td{padding:9px;border-bottom:1px solid #d9d2c4;text-align:left}a{color:inherit;font-weight:700} -@media(max-width:850px){.grid,.framing{grid-template-columns:1fr}.wide{grid-column:auto}.metrics{grid-template-columns:1fr 1fr}.wikilayout,.graphlayout{grid-template-columns:1fr}.wikinav{max-height:220px;border-right:0;border-bottom:1px solid var(--line)}.detail{border-left:0}.opportunity{grid-template-columns:1fr}.related{grid-template-columns:1fr}} +@media(max-width:850px){.grid,.framing{grid-template-columns:1fr}.wide{grid-column:auto}.metrics{grid-template-columns:1fr 1fr}.wikilayout,.graphlayout{grid-template-columns:1fr}.wikinav{max-height:220px;border-right:0;border-bottom:1px solid var(--line)}.detail{border-left:0}.opportunity,.candidate{grid-template-columns:1fr}.related{grid-template-columns:1fr}}
      AI Topic Scout · Living research intelligence

      LLM-refined research contract

      What this scout evaluates

      Dashboard structure

        Discovery design

        Scouting strategy

          -
          Corpus distribution

          Research categories

          -
          Scout history

          Accepted-paper growth

          +
          Corpus distribution

          Research categories by visualized papers

          +
          Scout history

          Category share over time

          +
          Candidate queue

          Discovered papers awaiting review

          Compiled knowledge base

          Research Wiki

          Association map

          Paper Graph

          LLM reasoning

          Underexplored areas & opportunities

          @@ -220,15 +313,22 @@ def build_data() -> dict: question.textContent=data.topic.research_question||data.topic.topic; dashboardsections.innerHTML=(data.topic.dashboard_sections||data.topic.taxonomy).map(x=>`
        • ${x}
          • `).join(""); scoutingstrategy.innerHTML=(data.topic.scouting_strategy||[]).map(x=>`
        • ${x}
          • `).join(""); -metrics.innerHTML=[["Accepted papers",data.paper_count],["Wiki pages",data.wiki.page_count],["Graph links",data.graph.edges.length],["Scout runs",data.runs.length]].map(x=>`
          ${x[0]}${x[1]}
          `).join(""); -categories.innerHTML=data.categories.map(x=>`
          ${x.label}${x.count}
          `).join(""); -const ts=document.getElementById("trend"),runs=data.runs,max=Math.max(1,...runs.map(x=>x.cumulative));let chart='';if(runs.length){const pts=runs.map((r,i)=>`${35+i*700/Math.max(1,runs.length-1)},${240-r.cumulative*210/max}`).join(" ");chart+=``;runs.forEach((r,i)=>chart+=`${r.date.slice(5)}`)}ts.innerHTML=chart; +document.getElementById("visualizedsource").textContent=`This section is rendered from ${data.visualized_source}.`; +document.getElementById("trendsource").textContent=data.runs.length ? `Showing category percentage across ${data.runs.length} scout run(s), based on cumulative accepted papers.` : "No scout history yet; run `make scout` to add the first dot."; +metrics.innerHTML=[["Accepted papers",data.paper_count],["Visualized papers",data.visualized_paper_count],["Wiki pages",data.wiki.page_count],["Graph links",data.graph.edges.length],["Scout runs",data.runs.length],["Scout tokens",data.scout_tokens],["Scout spend",`$${data.scout_spend_usd.toFixed(2)}`]].map(x=>`
          ${x[0]}${x[1]}
          `).join(""); +categories.innerHTML=data.categories.map(x=>`
          ${x.label}${x.count} (${Math.round(x.ratio*100)}%)
          `).join(""); +const candidateSummaryText = data.candidate_count ? `Latest scout found ${data.candidate_count} candidates${data.candidate_generated_at ? ` at ${data.candidate_generated_at}` : ""}. Recorded scout usage: ${data.candidate_cost.token_count} tokens, $${data.candidate_cost.money_cost_usd.toFixed(2)} ${data.candidate_cost.currency} via ${data.candidate_cost.provider}${data.candidate_cost.model ? ` (${data.candidate_cost.model})` : ""}.${data.candidate_cost.note ? ` ${data.candidate_cost.note}` : ""}` : "No candidates recorded yet. Run `make scout` to populate this section."; +document.getElementById("candidatesummary").textContent=candidateSummaryText; +const candidateItems=(data.candidates||[]).map((x,i)=>`
          ${String(i+1).padStart(2,"0")}
          ${x.relevance_score.toFixed(1)}${x.citation_count} cites

          ${x.title}

          ${x.year||""}

          ${x.id} · ${x.discovered_via?.[0]||"n/a"}

          ${x.relevance_reason||""}

          `).join(""); +document.getElementById("candidates").innerHTML=candidateItems||"

          No candidate rows loaded.

          "; +document.getElementById("trendlegend").innerHTML=data.categories.filter(x=>x.count||data.runs.some(r=>(r.cumulative_topics||{})[x.id])).map(x=>`${x.label}`).join(""); +const ts=document.getElementById("trend"),runs=data.runs,W=820,H=330,pad={l:42,r:16,t:12,b:40},visibleTrendCategories=data.categories.filter(x=>x.count||runs.some(r=>(r.cumulative_topic_ratios||{})[x.id])),trendMax=1;let chart=``;const tx=i=>pad.l+(runs.length===1?((W-pad.l-pad.r)/2):i*(W-pad.l-pad.r)/Math.max(1,runs.length-1)),ty=v=>H-pad.b-v*(H-pad.t-pad.b)/trendMax,dotOffset=(i,v,catId)=>{const same=visibleTrendCategories.filter(cat=>Math.abs((((runs[i].cumulative_topic_ratios||{})[cat.id]||0)-v))<1e-9);if(same.length<=1)return 0;const idx=same.findIndex(cat=>cat.id===catId);return (idx-(same.length-1)/2)*10;};for(let i=0;i<=4;i++){const v=i/4;chart+=`${Math.round(v*100)}%`;}if(runs.length){runs.forEach((r,i)=>{chart+=`${r.date.slice(5)}`;});visibleTrendCategories.forEach(cat=>{const pts=runs.map((r,i)=>`${tx(i)},${ty((r.cumulative_topic_ratios||{})[cat.id]||0)}`).join(" ");if(runs.length>1)chart+=``;runs.forEach((r,i)=>{const value=(r.cumulative_topic_ratios||{})[cat.id]||0,y=ty(value),x=tx(i)+dotOffset(i,value,cat.id);chart+=``;});});}ts.innerHTML=chart; const wp=Object.fromEntries(data.wiki.pages.map(x=>[x.id,x]));let current="overview",wq=""; function nav(){wikinav.innerHTML=data.wiki.pages.filter(x=>!wq||`${x.title} ${x.body}`.toLowerCase().includes(wq)).map(x=>``).join("")} function openPage(id){const p=wp[id];if(!p)return;current=id;nav();wikipage.innerHTML=`
          ${p.type}

          ${p.title}

          ${p.subtitle||""}

          ${p.body}

          ${p.source_url?`

          Open source ↗

          `:""}${p.assessment?`

          Scout assessment

          ${p.assessment}

          `:""}${p.related?.length?`

          Related papers

          ${p.related.map(x=>``).join("")}
          `:""}${p.links?.length?`

          Linked pages

          ${p.links.map(id=>wp[id]?``:"").join("")}
          `:""}`;wikipage.scrollTop=0} wiki.addEventListener("click",e=>{const b=e.target.closest("[data-page]");if(b)openPage(b.dataset.page)});wikisearch.oninput=e=>{wq=e.target.value.toLowerCase();nav()};nav();openPage("overview"); graphcat.innerHTML+=data.categories.map(x=>``).join(""); -const cv=graph,ctx=cv.getContext("2d"),catIndex=Object.fromEntries(data.categories.map((x,i)=>[x.id,i])),nodes=data.graph.nodes.map((n,i)=>{const a=catIndex[n.category]/Math.max(1,data.categories.length)*Math.PI*2,r=60+(i%8)*8;return{...n,x:Math.cos(a)*r,y:Math.sin(a)*r,vx:0,vy:0}}),nodeMap=Object.fromEntries(nodes.map(x=>[x.id,x])),edges=data.graph.edges.map(e=>({...e,a:nodeMap[e.source],b:nodeMap[e.target]}));let view={x:0,y:0,s:.65},filter="all",selected=null,frame=0; +const cv=graph,ctx=cv.getContext("2d"),catIndex=Object.fromEntries(data.categories.map((x,i)=>[x.id,i])),nodes=data.graph.nodes.map((n,i)=>{const a=catIndex[n.category]/Math.max(1,data.categories.length)*Math.PI*2,r=60+(i%8)*8;return{...n,x:Math.cos(a)*r,y:Math.sin(a)*r,vx:0,vy:0}}),nodeMap=Object.fromEntries(nodes.map(x=>[x.id,x])),edges=data.graph.edges.map(e=>({...e,a:nodeMap[e.source],b:nodeMap[e.target]}));let view={x:0,y:0,s:.5},filter="all",selected=null,frame=0; function resize(){const r=cv.getBoundingClientRect(),d=Math.min(devicePixelRatio||1,2);cv.width=r.width*d;cv.height=r.height*d;ctx.setTransform(d,0,0,d,0,0)}function pos(n){const r=cv.getBoundingClientRect();return{x:r.width/2+view.x+n.x*view.s,y:r.height/2+view.y+n.y*view.s}}function visible(n){return filter==="all"||n.category===filter} function draw(){const r=cv.getBoundingClientRect();ctx.clearRect(0,0,r.width,r.height);edges.forEach(e=>{if(!visible(e.a)||!visible(e.b))return;const a=pos(e.a),b=pos(e.b);ctx.beginPath();ctx.moveTo(a.x,a.y);ctx.lineTo(b.x,b.y);ctx.strokeStyle=selected&&(e.a===selected||e.b===selected)?"#ddd":"#60706b44";ctx.stroke()});nodes.forEach(n=>{if(!visible(n))return;const p=pos(n),rad=5+Math.sqrt(n.degree+1)*2;ctx.beginPath();ctx.arc(p.x,p.y,rad,0,Math.PI*2);ctx.fillStyle=byCat[n.category]?.color||"#aaa";ctx.fill();if(n===selected){ctx.strokeStyle="#fff";ctx.lineWidth=2;ctx.stroke();ctx.fillStyle="#fff";ctx.font="12px sans-serif";ctx.fillText(n.title,p.x+rad+5,p.y)}})} function sim(){if(frame++<360){for(let i=0;i{let dx=e.b.x-e.a.x,dy=e.b.y-e.a.y,d=Math.max(1,Math.hypot(dx,dy)),f=(d-42)*.003;e.a.vx+=dx/d*f;e.a.vy+=dy/d*f;e.b.vx-=dx/d*f;e.b.vy-=dy/d*f});nodes.forEach(n=>{n.vx+=-n.x*.001;n.vy+=-n.y*.001;n.vx*=.87;n.vy*=.87;n.x+=n.vx;n.y+=n.vy})}draw();requestAnimationFrame(sim)} diff --git a/scripts/costs.py b/scripts/costs.py new file mode 100644 index 0000000..6593ed4 --- /dev/null +++ b/scripts/costs.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python3 +"""Helpers for recording scouting token and money cost.""" + +from __future__ import annotations + + +def zero_cost(provider: str = "openalex") -> dict: + return { + "provider": provider, + "token_count": 0, + "money_cost_usd": 0.0, + "currency": "USD", + "note": "OpenAlex discovery does not invoke a model; token usage is zero.", + } + + +def usage_cost( + *, + provider: str, + model: str | None, + input_tokens: int = 0, + output_tokens: int = 0, + reasoning_tokens: int = 0, + money_cost_usd: float = 0.0, + currency: str = "USD", + note: str = "", +) -> dict: + token_count = int(input_tokens) + int(output_tokens) + int(reasoning_tokens) + payload = { + "provider": provider, + "model": model, + "token_count": token_count, + "input_tokens": int(input_tokens), + "output_tokens": int(output_tokens), + "reasoning_tokens": int(reasoning_tokens), + "money_cost_usd": float(money_cost_usd), + "currency": currency, + } + if note: + payload["note"] = note + return payload diff --git a/scripts/init_topic.py b/scripts/init_topic.py index 9e8417c..8b03ba4 100644 --- a/scripts/init_topic.py +++ b/scripts/init_topic.py @@ -10,7 +10,7 @@ from datetime import date from intent_refiner import IntentRefinementError, refine_intent -from workspace import DATA_DIR, PAPERS_PATH, ROOT, TOPIC_CONFIG, write_json +from workspace import DATA_DIR, PAPERS_PATH, ROOT, TOPIC_AGENTS_PATH, TOPIC_CONFIG, write_json def slugify(value: str) -> str: @@ -41,10 +41,10 @@ def build_queries(topic: str, include: list[str], evidence_types: list[str]) -> return list(dict.fromkeys(queries)) -def render_agents(config: dict) -> str: +def render_topic_agents(config: dict) -> str: include = ", ".join(config["include"]) or "the defined topic" exclude = ", ".join(config["exclude"]) or "none" - return f"""# AGENTS.md + return f"""# TOPIC_AGENTS.md ## Mission @@ -95,7 +95,7 @@ def render_scout_skill(config: dict) -> str: # Topic Paper Scout -1. Read `topic.json`, `data/papers.json`, and `AGENTS.md`. +1. Read `AGENTS.md`, `TOPIC_AGENTS.md`, `topic.json`, and `data/papers.json`. 2. Run `python3 scripts/scout.py`. 3. Review `data/candidates.json` against inclusion and exclusion rules. 4. Verify every accepted paper's identifier, URL, year, and abstract. @@ -105,6 +105,8 @@ def render_scout_skill(config: dict) -> str: Search within {config['years']['from']}-{config['years']['to']}. Prioritize: {", ".join(config['evidence_types'])}. +Scout uses model-backed candidate scoring by default. Report token_count and money_cost_usd +for every scouting run, even when the cost is zero. ## Topic-Specific Scouting Strategy @@ -120,7 +122,7 @@ def render_gap_skill(config: dict) -> str: # Analyze Research Gaps -1. Read `topic.json`, `data/papers.json`, `reports/research_report.md`, and `data/dashboard.json`. +1. Read `AGENTS.md`, `TOPIC_AGENTS.md`, `topic.json`, `data/papers.json`, `reports/research_report.md`, and `data/dashboard.json`. 2. Assess coverage scarcity, graph structure, evidence maturity, contradictions, and decision importance. 3. Separate `corpus_gap`, `field_gap`, `evidence_gap`, and `translation_gap`. 4. Rank 3-6 opportunities. @@ -168,7 +170,7 @@ def render_role(role: str, objective: str, config: dict) -> str: ## Required Behavior -- Read `AGENTS.md` and `topic.json` before acting. +- Read `AGENTS.md`, `TOPIC_AGENTS.md`, and `topic.json` before acting. - Treat candidates as untrusted until reviewed. - Preserve source provenance and stable identifiers. - Write only the outputs assigned to this role. @@ -180,8 +182,8 @@ def render_scout_prompt(config: dict) -> str: strategy = "\n".join(f" - {item}" for item in config["scouting_strategy"]) return f"""AI Topic Scout scheduled run for: {config['topic']} -1. Read `AGENTS.md`, `topic.json`, and `skills/topic-paper-scout/SKILL.md`. -2. Run the configured searches and scholarly graph expansion. +1. Read `AGENTS.md`, `TOPIC_AGENTS.md`, `topic.json`, and `skills/topic-paper-scout/SKILL.md`. +2. Run the configured searches, scholarly graph expansion, and model-backed candidate scoring. 3. Compare candidates against `data/papers.json`. 4. Apply inclusion rules: {", ".join(config['include'])}. 5. Apply exclusions: {", ".join(config['exclude']) or "none"}. @@ -195,6 +197,7 @@ def render_scout_prompt(config: dict) -> str: - run `make dashboard`; - commit and report only the accepted additions. 9. If no paper is accepted, do not rewrite tracked artifacts and return no update. +10. Include token_count and money_cost_usd in every scouting report and acceptance record. Topic-specific strategy: {strategy} @@ -205,7 +208,7 @@ def initialize(config: dict) -> None: write_json(TOPIC_CONFIG, config) if not PAPERS_PATH.exists(): write_json(PAPERS_PATH, {"topic": config["topic"], "papers": [], "scout_runs": []}) - (ROOT / "AGENTS.md").write_text(render_agents(config), encoding="utf-8") + TOPIC_AGENTS_PATH.write_text(render_topic_agents(config), encoding="utf-8") scout_dir = ROOT / "skills" / "topic-paper-scout" gap_dir = ROOT / "skills" / "analyze-research-gaps" scout_dir.mkdir(parents=True, exist_ok=True) @@ -366,6 +369,8 @@ def main() -> int: "scouting_strategy": refined["scouting_strategy"], "intent_refinement_provider": "offline" if args.offline else args.provider, "intent_refinement_model": refinement_model, + "scout_provider": args.provider, + "scout_model": args.model or refinement_model, "created_at": date.today().isoformat(), } initialize(config) diff --git a/scripts/orchestrate.py b/scripts/orchestrate.py index 4761cbd..793c5fc 100644 --- a/scripts/orchestrate.py +++ b/scripts/orchestrate.py @@ -77,7 +77,7 @@ def emit(mode: str, config: dict) -> dict: "mode": mode, "topic": config["topic"], "coordinator_instruction": ( - "Read AGENTS.md and topic.json. Assign only accepted research-contract tasks. " + "Read AGENTS.md, TOPIC_AGENTS.md, and topic.json. Assign only accepted research-contract tasks. " "Do not publish when no new paper is accepted." ), "tasks": tasks(config), @@ -105,6 +105,7 @@ def main() -> int: print("No accepted new papers; report and dashboard remain unchanged.") return 0 subprocess.run([sys.executable, "scripts/build_corpus.py"], cwd=ROOT, check=True) + subprocess.run([sys.executable, "scripts/analyze_research_gaps.py"], cwd=ROOT, check=True) subprocess.run([sys.executable, "scripts/build_dashboard.py"], cwd=ROOT, check=True) return 0 diff --git a/scripts/scout.py b/scripts/scout.py index 592deeb..2094998 100644 --- a/scripts/scout.py +++ b/scripts/scout.py @@ -4,11 +4,16 @@ from __future__ import annotations import argparse +import os from datetime import datetime, timezone +from costs import zero_cost from paper_graph import discover +from scout_llm import ScoutModelError, score_candidates from workspace import CANDIDATES_PATH, PAPERS_PATH, load_json, load_topic, write_json +DEFAULT_ACCEPT_SCORE = 7.0 + def main() -> int: parser = argparse.ArgumentParser() @@ -21,23 +26,108 @@ def main() -> int: default=None, help="Accept candidates at or above this score when topic.json allows auto-approval", ) + parser.add_argument( + "--provider", + choices=["codex", "api"], + help="Scout scoring provider. Defaults to topic.json or codex.", + ) + parser.add_argument("--model", help="Model override for scout scoring") + parser.add_argument( + "--llm-candidates", + type=int, + default=40, + help="How many discovered candidates to rescore with the model", + ) + parser.add_argument( + "--offline", + action="store_true", + help="Skip model-backed scout scoring and keep OpenAlex-only heuristic ranking", + ) args = parser.parse_args() config = load_topic() + accept_score = args.accept_score + if accept_score is None and not config["approval_required"]: + accept_score = DEFAULT_ACCEPT_SCORE result = discover(config, args.query, args.seed_limit, args.neighbors) existing = load_json(PAPERS_PATH, {"papers": []}) known = {paper["id"] for paper in existing.get("papers", [])} result["generated_at"] = datetime.now(timezone.utc).isoformat() + if args.offline: + result["cost"] = zero_cost() + result["scout_provider"] = "openalex" + else: + provider = ( + args.provider + or config.get("scout_provider") + or os.environ.get("TOPIC_SCOUT_SCOUT_PROVIDER") + or "codex" + ) + if provider == "openalex": + result["cost"] = zero_cost() + result["scout_provider"] = "openalex" + result["new_candidate_count"] = sum( + candidate["id"] not in known for candidate in result["candidates"] + ) + write_json(CANDIDATES_PATH, result) + existing.setdefault("scout_runs", []).append( + { + "date": result["generated_at"][:10], + "queries": result["queries"], + "accepted_ids": [], + "accepted_count": 0, + "candidate_count": len(result["candidates"]), + "cost": result["cost"], + } + ) + write_json(PAPERS_PATH, existing) + print( + f"Discovered {len(result['candidates'])} candidates " + f"({result['new_candidate_count']} not in corpus)." + ) + print("Scout cost: 0 tokens, $0.00 USD.") + print("topic.json requested the legacy OpenAlex-only scout path; rerun with --provider codex or --provider api to require an LLM.") + return 0 + llm_candidates = result["candidates"][: max(0, args.llm_candidates)] + try: + updates, usage = score_candidates( + config, + llm_candidates, + provider=provider, + model=args.model or config.get("scout_model"), + ) + except ScoutModelError as exc: + raise SystemExit(f"Scout scoring failed: {exc}") + for candidate in result["candidates"]: + candidate["heuristic_relevance_score"] = candidate.get("relevance_score", 0) + candidate["heuristic_relevance_reason"] = candidate.get("relevance_reason", "") + update = updates.get(candidate["id"]) + if update: + candidate.update(update) + result["candidates"].sort( + key=lambda item: (-item.get("relevance_score", 0), -item.get("citation_count", 0), -(item.get("year") or 0)) + ) + result["cost"] = usage + result["scout_provider"] = provider result["new_candidate_count"] = sum( candidate["id"] not in known for candidate in result["candidates"] ) write_json(CANDIDATES_PATH, result) accepted = [] - if not config["approval_required"] and args.accept_score is not None: + scout_run = { + "date": result["generated_at"][:10], + "queries": result["queries"], + "accepted_ids": [], + "accepted_count": 0, + "candidate_count": len(result["candidates"]), + "cost": result["cost"], + } + existing.setdefault("scout_runs", []).append(scout_run) + if not config["approval_required"] and accept_score is not None: accepted = [ candidate for candidate in result["candidates"] if candidate["id"] not in known - and candidate["relevance_score"] >= args.accept_score + and candidate["relevance_score"] >= accept_score and candidate["abstract"] ] if accepted: @@ -51,14 +141,11 @@ def main() -> int: } ) existing["papers"].extend(accepted) - existing.setdefault("scout_runs", []).append( - { - "date": result["generated_at"][:10], - "queries": result["queries"], - "accepted_ids": [paper["id"] for paper in accepted], - } - ) + scout_run["accepted_ids"] = [paper["id"] for paper in accepted] + scout_run["accepted_count"] = len(accepted) write_json(PAPERS_PATH, existing) + else: + write_json(PAPERS_PATH, existing) print( f"Discovered {len(result['candidates'])} candidates " @@ -67,7 +154,15 @@ def main() -> int: if config["approval_required"]: print(f"Review {CANDIDATES_PATH}; acceptance requires a librarian or human.") elif accepted: - print(f"Accepted {len(accepted)} candidates at score >= {args.accept_score}.") + print(f"Accepted {len(accepted)} candidates at score >= {accept_score}.") + elif not config["approval_required"]: + print(f"No candidates met the auto-accept threshold of {accept_score}.") + print( + f"Scout cost: {result['cost']['token_count']} tokens, " + f"${result['cost']['money_cost_usd']:.2f} {result['cost']['currency']}." + ) + if result["cost"].get("note"): + print(result["cost"]["note"]) return 0 diff --git a/scripts/scout_llm.py b/scripts/scout_llm.py new file mode 100644 index 0000000..60a75ee --- /dev/null +++ b/scripts/scout_llm.py @@ -0,0 +1,279 @@ +#!/usr/bin/env python3 +"""Model-backed candidate evaluation for scout runs.""" + +from __future__ import annotations + +import json +import os +import shutil +import subprocess +import tempfile +import urllib.error +import urllib.request +from pathlib import Path + +from costs import usage_cost +from intent_refiner import DEFAULT_MODEL, RESPONSES_URL, _output_text + + +SCOUT_SCHEMA = { + "type": "object", + "properties": { + "candidates": { + "type": "array", + "items": { + "type": "object", + "properties": { + "id": {"type": "string", "minLength": 3}, + "relevance_score": {"type": "number", "minimum": 0, "maximum": 10}, + "relevance_reason": {"type": "string", "minLength": 5}, + }, + "required": ["id", "relevance_score", "relevance_reason"], + "additionalProperties": False, + }, + } + }, + "required": ["candidates"], + "additionalProperties": False, +} + +SCOUT_INSTRUCTIONS = ( + "You are a research scout reviewer. Score candidate papers against the provided research " + "contract. Use the candidate title, abstract, topics, and provenance only. Be conservative. " + "Prefer low scores when relevance is ambiguous. Penalize items that match excluded scope. " + "Return every provided candidate id exactly once with a relevance_score from 0 to 10 and a " + "short concrete reason grounded in the candidate text." +) + + +class ScoutModelError(RuntimeError): + """Raised when scout candidate evaluation fails.""" + + +def _candidate_payload(config: dict, candidates: list[dict]) -> str: + compact = [] + for item in candidates: + compact.append( + { + "id": item["id"], + "title": item.get("title", ""), + "year": item.get("year"), + "abstract": (item.get("abstract") or "")[:1800], + "topics": item.get("topics", [])[:8], + "discovered_via": item.get("discovered_via", [])[:4], + "citation_count": item.get("citation_count", 0), + "heuristic_relevance_score": item.get("relevance_score", 0), + "heuristic_relevance_reason": item.get("relevance_reason", ""), + } + ) + return json.dumps( + { + "topic": config["topic"], + "research_question": config.get("research_question"), + "goal": config.get("goal"), + "include": config.get("include", []), + "exclude": config.get("exclude", []), + "taxonomy": config.get("taxonomy", []), + "candidates": compact, + }, + ensure_ascii=False, + ) + + +def _usage_cost_from_responses(provider: str, model: str, body: dict) -> dict: + usage = body.get("usage") or {} + input_tokens = int(usage.get("input_tokens", 0) or 0) + output_tokens = int(usage.get("output_tokens", 0) or 0) + output_details = usage.get("output_tokens_details") or {} + reasoning_tokens = int(output_details.get("reasoning_tokens", 0) or 0) + note = "" + if provider == "api": + note = ( + "Token usage is recorded from the Responses API. USD cost defaults to 0 unless " + "you map model pricing separately." + ) + return usage_cost( + provider=provider, + model=model, + input_tokens=input_tokens, + output_tokens=output_tokens, + reasoning_tokens=reasoning_tokens, + money_cost_usd=0.0, + note=note, + ) + + +def score_candidates_api( + config: dict, + candidates: list[dict], + *, + api_key: str | None = None, + model: str | None = None, + urlopen=urllib.request.urlopen, +) -> tuple[dict[str, dict], dict]: + key = api_key or os.environ.get("OPENAI_API_KEY") + if not key: + raise ScoutModelError( + "OPENAI_API_KEY is required for model-backed scout scoring. " + "Set it or rerun scout with --offline." + ) + selected_model = model or os.environ.get("TOPIC_SCOUT_SCOUT_MODEL") or os.environ.get( + "TOPIC_SCOUT_MODEL", + DEFAULT_MODEL, + ) + payload = { + "model": selected_model, + "store": False, + "instructions": SCOUT_INSTRUCTIONS, + "input": _candidate_payload(config, candidates), + "reasoning": {"effort": "low"}, + "text": { + "verbosity": "low", + "format": { + "type": "json_schema", + "name": "scout_evaluation", + "strict": True, + "schema": SCOUT_SCHEMA, + }, + }, + } + request = urllib.request.Request( + RESPONSES_URL, + data=json.dumps(payload).encode("utf-8"), + headers={ + "Authorization": f"Bearer {key}", + "Content-Type": "application/json", + }, + method="POST", + ) + try: + with urlopen(request, timeout=120) as response: + body = json.loads(response.read().decode("utf-8")) + except urllib.error.HTTPError as exc: + detail = exc.read().decode("utf-8", errors="replace") + raise ScoutModelError(f"OpenAI scout scoring failed with HTTP {exc.code}: {detail}") from exc + except (urllib.error.URLError, TimeoutError, json.JSONDecodeError) as exc: + raise ScoutModelError(f"OpenAI scout scoring failed: {exc}") from exc + + try: + parsed = json.loads(_output_text(body)) + except json.JSONDecodeError as exc: + raise ScoutModelError("OpenAI scout scoring returned invalid structured JSON") from exc + mapping = { + row["id"]: { + "relevance_score": float(row["relevance_score"]), + "relevance_reason": row["relevance_reason"].strip(), + } + for row in parsed.get("candidates", []) + } + return mapping, _usage_cost_from_responses("api", selected_model, body) + + +def score_candidates_codex( + config: dict, + candidates: list[dict], + *, + model: str | None = None, + cwd: Path | str | None = None, + run=subprocess.run, +) -> tuple[dict[str, dict], dict]: + executable = shutil.which("codex") + if not executable: + raise ScoutModelError( + "Codex CLI is not installed. Install and sign in to Codex, use --provider api, " + "or rerun scout with --offline." + ) + prompt = ( + f"{SCOUT_INSTRUCTIONS}\n\n" + "Return only the requested structured output. Do not inspect files, run commands, " + "or modify the workspace.\n\n" + f"Input:\n{_candidate_payload(config, candidates)}" + ) + with tempfile.TemporaryDirectory(prefix="topic-scout-score-") as directory: + temp_dir = Path(directory) + schema_path = temp_dir / "schema.json" + output_path = temp_dir / "result.json" + schema_path.write_text(json.dumps(SCOUT_SCHEMA), encoding="utf-8") + command = [ + executable, + "exec", + "--ephemeral", + "--ignore-user-config", + "--ignore-rules", + "--sandbox", + "read-only", + "--color", + "never", + "--json", + "--output-schema", + str(schema_path), + "--output-last-message", + str(output_path), + ] + if model: + command.extend(["--model", model]) + command.append(prompt) + try: + result = run( + command, + cwd=str(cwd) if cwd else None, + stdin=subprocess.DEVNULL, + capture_output=True, + text=True, + timeout=240, + ) + except (OSError, subprocess.TimeoutExpired) as exc: + raise ScoutModelError(f"Codex scout scoring failed: {exc}") from exc + if result.returncode != 0: + detail = (result.stderr or result.stdout).strip() + raise ScoutModelError(f"Codex scout scoring failed: {detail or 'unknown error'}") + output = output_path.read_text(encoding="utf-8") if output_path.exists() else "" + try: + parsed = json.loads(output) + except json.JSONDecodeError as exc: + raise ScoutModelError("Codex scout scoring returned invalid structured JSON") from exc + usage = {} + for line in (result.stdout or "").splitlines(): + line = line.strip() + if not line or not line.startswith("{"): + continue + try: + event = json.loads(line) + except json.JSONDecodeError: + continue + if event.get("type") == "turn.completed": + usage = event.get("usage") or {} + mapping = { + row["id"]: { + "relevance_score": float(row["relevance_score"]), + "relevance_reason": row["relevance_reason"].strip(), + } + for row in parsed.get("candidates", []) + } + selected_model = f"codex-cli:{model or 'configured-model'}" + return mapping, usage_cost( + provider="codex", + model=selected_model, + input_tokens=int(usage.get("input_tokens", 0) or 0), + output_tokens=int(usage.get("output_tokens", 0) or 0), + reasoning_tokens=int(usage.get("reasoning_output_tokens", 0) or 0), + money_cost_usd=0.0, + note="Codex CLI reports token usage but does not expose billable USD cost in this workflow.", + ) + + +def score_candidates( + config: dict, + candidates: list[dict], + *, + provider: str = "codex", + model: str | None = None, + cwd: Path | str | None = None, + urlopen=urllib.request.urlopen, + run=subprocess.run, +) -> tuple[dict[str, dict], dict]: + if provider == "codex": + return score_candidates_codex(config, candidates, model=model, cwd=cwd, run=run) + if provider == "api": + return score_candidates_api(config, candidates, model=model, urlopen=urlopen) + raise ScoutModelError(f"Unsupported scout provider: {provider}") diff --git a/scripts/workspace.py b/scripts/workspace.py index ced8074..f6e25b5 100644 --- a/scripts/workspace.py +++ b/scripts/workspace.py @@ -10,6 +10,7 @@ ROOT = Path(os.environ.get("TOPIC_SCOUT_ROOT", Path(__file__).resolve().parents[1])).resolve() TOPIC_CONFIG = ROOT / "topic.json" +TOPIC_AGENTS_PATH = ROOT / "TOPIC_AGENTS.md" DATA_DIR = ROOT / "data" REPORTS_DIR = ROOT / "reports" PAPERS_PATH = DATA_DIR / "papers.json" diff --git a/skills/setup-topic-scout/SKILL.md b/skills/setup-topic-scout/SKILL.md index 9575f6a..1b80a4f 100644 --- a/skills/setup-topic-scout/SKILL.md +++ b/skills/setup-topic-scout/SKILL.md @@ -33,7 +33,7 @@ The refined contract must contain: Verify that initialization creates: - `topic.json` -- `AGENTS.md` +- `TOPIC_AGENTS.md` - `agents/*.md` - `skills/topic-paper-scout/SKILL.md` - `skills/analyze-research-gaps/SKILL.md` diff --git a/tests/test_core.py b/tests/test_core.py index 98badc3..e477d79 100644 --- a/tests/test_core.py +++ b/tests/test_core.py @@ -5,15 +5,20 @@ import tempfile import unittest from pathlib import Path +from unittest.mock import patch ROOT = Path(__file__).resolve().parents[1] sys.path.insert(0, str(ROOT / "scripts")) from build_corpus import classify from build_dashboard import graph_data, wiki_data +from costs import usage_cost, zero_cost from init_topic import build_queries, parse_years, slugify from intent_refiner import refine_intent, refine_intent_codex from paper_graph import Candidate, relevance +import scout as scout_module +import analyze_research_gaps as gaps_module +from scout_llm import score_candidates_api, score_candidates_codex CONFIG = { @@ -85,7 +90,7 @@ def test_noninteractive_initialization_generates_agent_workspace(self): ) root = Path(directory) self.assertTrue((root / "topic.json").exists()) - self.assertTrue((root / "AGENTS.md").exists()) + self.assertTrue((root / "TOPIC_AGENTS.md").exists()) self.assertTrue((root / "agents" / "graph-scout.md").exists()) self.assertTrue((root / "skills" / "topic-paper-scout" / "SKILL.md").exists()) topic = json.loads((root / "topic.json").read_text()) @@ -144,6 +149,546 @@ def fake_urlopen(request, timeout): self.assertEqual(captured["payload"]["text"]["format"]["type"], "json_schema") self.assertFalse(captured["payload"]["store"]) + def test_cost_block_defaults_to_zero_for_openalex_scouting(self): + cost = zero_cost() + self.assertEqual(cost["token_count"], 0) + self.assertEqual(cost["money_cost_usd"], 0.0) + self.assertEqual(cost["currency"], "USD") + + def test_usage_cost_sums_input_output_and_reasoning(self): + cost = usage_cost( + provider="codex", + model="codex-cli:test", + input_tokens=100, + output_tokens=25, + reasoning_tokens=5, + ) + self.assertEqual(cost["token_count"], 130) + self.assertEqual(cost["input_tokens"], 100) + self.assertEqual(cost["output_tokens"], 25) + self.assertEqual(cost["reasoning_tokens"], 5) + + def test_dashboard_payload_includes_candidates(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + (root / "data").mkdir(parents=True, exist_ok=True) + (root / "reports").mkdir(parents=True, exist_ok=True) + (root / "topic.json").write_text( + json.dumps( + { + "topic": "Detecting Discriminatory AI in Hiring", + "goal": "Track bias detection methods", + "audience": "researchers", + "taxonomy": ["methods", "systems"], + "include": ["hiring", "fairness"], + "exclude": [], + "years": {"from": 2023, "to": 2026}, + } + ), + encoding="utf-8", + ) + (root / "data" / "papers.json").write_text( + json.dumps({"papers": [], "scout_runs": []}), + encoding="utf-8", + ) + (root / "data" / "candidates.json").write_text( + json.dumps( + { + "generated_at": "2026-06-13T00:00:00Z", + "cost": {"token_count": 12, "money_cost_usd": 0.03, "currency": "USD"}, + "queries": ["query"], + "candidates": [ + { + "id": "openalex:1", + "title": "Candidate One", + "year": 2025, + "url": "https://example.com", + "citation_count": 4, + "relevance_score": 1.5, + "relevance_reason": "example", + "topics": ["fairness"], + "discovered_via": ["query:test"], + } + ], + } + ), + encoding="utf-8", + ) + env = dict(os.environ, TOPIC_SCOUT_ROOT=directory) + subprocess.run( + [sys.executable, str(ROOT / "scripts" / "build_dashboard.py")], + check=True, + env=env, + capture_output=True, + text=True, + ) + payload = json.loads((root / "data" / "dashboard.json").read_text()) + self.assertEqual(payload["candidate_count"], 1) + self.assertEqual(payload["candidate_cost"]["token_count"], 12) + self.assertEqual(payload["candidates"][0]["title"], "Candidate One") + self.assertTrue(any(page["type"] == "candidate" for page in payload["wiki"]["pages"])) + + def test_dashboard_uses_candidates_for_visuals_when_no_papers_are_accepted(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + (root / "data").mkdir(parents=True, exist_ok=True) + (root / "reports").mkdir(parents=True, exist_ok=True) + (root / "topic.json").write_text( + json.dumps( + { + "topic": "Detecting Discriminatory AI in Hiring", + "goal": "Track bias detection methods", + "audience": "researchers", + "taxonomy": ["methods", "systems"], + "include": ["hiring", "fairness"], + "exclude": [], + "years": {"from": 2023, "to": 2026}, + } + ), + encoding="utf-8", + ) + (root / "data" / "papers.json").write_text( + json.dumps({"papers": [], "scout_runs": []}), + encoding="utf-8", + ) + (root / "data" / "candidates.json").write_text( + json.dumps( + { + "generated_at": "2026-06-13T00:00:00Z", + "cost": {"token_count": 12, "money_cost_usd": 0.03, "currency": "USD"}, + "queries": ["query"], + "candidates": [ + { + "id": "openalex:1", + "title": "Candidate One", + "year": 2025, + "url": "https://example.com", + "citation_count": 4, + "relevance_score": 1.5, + "relevance_reason": "example", + "topics": ["fairness"], + "discovered_via": ["query:test"], + "abstract": "fairness in hiring systems", + } + ], + } + ), + encoding="utf-8", + ) + env = dict(os.environ, TOPIC_SCOUT_ROOT=directory) + subprocess.run( + [sys.executable, str(ROOT / "scripts" / "build_dashboard.py")], + check=True, + env=env, + capture_output=True, + text=True, + ) + payload = json.loads((root / "data" / "dashboard.json").read_text()) + self.assertEqual(payload["visualized_paper_count"], 1) + self.assertEqual(payload["visualized_source"], "discovered candidates") + self.assertGreater(len(payload["graph"]["nodes"]), 0) + self.assertGreater(payload["categories"][0]["count"] + payload["categories"][1]["count"], 0) + + def test_dashboard_records_category_history_for_scout_runs(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + (root / "data").mkdir(parents=True, exist_ok=True) + (root / "reports").mkdir(parents=True, exist_ok=True) + (root / "topic.json").write_text( + json.dumps( + { + "topic": "Detecting Discriminatory AI in Hiring", + "goal": "Track bias detection methods", + "audience": "researchers", + "taxonomy": ["methods", "systems"], + "include": ["hiring", "fairness"], + "exclude": [], + "years": {"from": 2023, "to": 2026}, + } + ), + encoding="utf-8", + ) + (root / "data" / "papers.json").write_text( + json.dumps( + { + "papers": [ + { + "id": "openalex:1", + "title": "Candidate One", + "year": 2025, + "url": "https://example.com/1", + "citation_count": 4, + "abstract": "fairness in hiring systems", + "topics": ["fairness"], + }, + { + "id": "openalex:2", + "title": "Candidate Two", + "year": 2025, + "url": "https://example.com/2", + "citation_count": 2, + "abstract": "hiring workflow systems", + "topics": ["workflow"], + }, + ], + "scout_runs": [ + { + "date": "2026-06-13", + "accepted_ids": ["openalex:1", "openalex:2"], + "accepted_count": 2, + "candidate_count": 10, + "cost": {"token_count": 0, "money_cost_usd": 0.0, "currency": "USD"}, + } + ], + } + ), + encoding="utf-8", + ) + (root / "data" / "candidates.json").write_text( + json.dumps({"generated_at": "2026-06-13T00:00:00Z", "candidates": []}), + encoding="utf-8", + ) + env = dict(os.environ, TOPIC_SCOUT_ROOT=directory) + subprocess.run( + [sys.executable, str(ROOT / "scripts" / "build_dashboard.py")], + check=True, + env=env, + capture_output=True, + text=True, + ) + payload = json.loads((root / "data" / "dashboard.json").read_text()) + self.assertEqual(len(payload["runs"]), 1) + self.assertIn("accepted_topic_counts", payload["runs"][0]) + self.assertIn("cumulative_topics", payload["runs"][0]) + self.assertIn("cumulative_topic_ratios", payload["runs"][0]) + self.assertEqual( + sum(payload["runs"][0]["accepted_topic_counts"].values()), + payload["runs"][0]["accepted"], + ) + self.assertEqual( + payload["runs"][0]["cumulative_topics"], + payload["runs"][0]["accepted_topic_counts"], + ) + self.assertAlmostEqual( + sum(payload["runs"][0]["cumulative_topic_ratios"].values()), + 1.0, + ) + + def test_scout_records_history_even_when_nothing_is_accepted(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + (root / "data").mkdir(parents=True, exist_ok=True) + papers_path = root / "data" / "papers.json" + candidates_path = root / "data" / "candidates.json" + papers_path.write_text( + json.dumps({"papers": [], "scout_runs": []}), + encoding="utf-8", + ) + fake_result = { + "topic": "AI theorem proving", + "queries": ["q"], + "candidates": [ + { + "id": "openalex:1", + "title": "Formal proof search", + "year": 2025, + "url": "https://example.com", + "doi": None, + "abstract": "Proof search with verifier feedback.", + "citation_count": 10, + "topics": ["proof search"], + "discovered_via": ["query:q"], + "relevance_score": 8.0, + "relevance_reason": "aligned", + } + ], + "edges": [], + } + fake_topic = { + "topic": "AI theorem proving", + "goal": "Track proof systems", + "audience": "researchers", + "include": ["proof search", "formal verification"], + "exclude": [], + "years": {"from": 2023, "to": 2026}, + "taxonomy": ["proof search", "verification", "benchmarks"], + "approval_required": True, + } + + def fake_load_json(path, default): + if not path.exists(): + return default + return json.loads(path.read_text(encoding="utf-8")) + + def fake_write_json(path, payload): + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(payload, indent=2, ensure_ascii=True) + "\n", encoding="utf-8") + + with patch.object(scout_module, "discover", return_value=fake_result): + with patch.object(scout_module, "load_topic", return_value=fake_topic): + with patch.object(scout_module, "load_json", side_effect=fake_load_json): + with patch.object(scout_module, "write_json", side_effect=fake_write_json): + with patch.object(scout_module, "PAPERS_PATH", papers_path): + with patch.object(scout_module, "CANDIDATES_PATH", candidates_path): + with patch.object(sys, "argv", ["scout.py", "--offline"]): + self.assertEqual(scout_module.main(), 0) + + corpus = json.loads(papers_path.read_text()) + self.assertEqual(len(corpus["scout_runs"]), 1) + self.assertEqual(corpus["scout_runs"][0]["accepted_count"], 0) + self.assertGreaterEqual(corpus["scout_runs"][0]["candidate_count"], 0) + + def test_scout_auto_accepts_by_default_when_approval_not_required(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + (root / "data").mkdir(parents=True, exist_ok=True) + papers_path = root / "data" / "papers.json" + candidates_path = root / "data" / "candidates.json" + papers_path.write_text( + json.dumps({"papers": [], "scout_runs": []}), + encoding="utf-8", + ) + candidates_path.write_text( + json.dumps({"candidates": []}), + encoding="utf-8", + ) + fake_result = { + "topic": "AI theorem proving", + "queries": ["q"], + "candidates": [ + { + "id": "openalex:1", + "title": "Formal proof search", + "year": 2025, + "url": "https://example.com", + "doi": None, + "abstract": "Proof search with verifier feedback.", + "citation_count": 10, + "topics": ["proof search"], + "discovered_via": ["query:q"], + "relevance_score": 8.0, + "relevance_reason": "aligned", + } + ], + "edges": [], + } + fake_topic = { + "topic": "AI theorem proving", + "goal": "Track proof systems", + "audience": "researchers", + "include": ["proof search", "formal verification"], + "exclude": [], + "years": {"from": 2023, "to": 2026}, + "taxonomy": ["proof search", "verification", "benchmarks"], + "approval_required": False, + } + + def fake_load_json(path, default): + if not path.exists(): + return default + return json.loads(path.read_text(encoding="utf-8")) + + def fake_write_json(path, payload): + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(payload, indent=2, ensure_ascii=True) + "\n", encoding="utf-8") + + with patch.object(scout_module, "discover", return_value=fake_result): + with patch.object(scout_module, "load_topic", return_value=fake_topic): + with patch.object(scout_module, "load_json", side_effect=fake_load_json): + with patch.object(scout_module, "write_json", side_effect=fake_write_json): + with patch.object(scout_module, "PAPERS_PATH", papers_path): + with patch.object(scout_module, "CANDIDATES_PATH", candidates_path): + with patch.object(sys, "argv", ["scout.py", "--offline"]): + self.assertEqual(scout_module.main(), 0) + corpus = json.loads(papers_path.read_text()) + self.assertEqual(len(corpus["papers"]), 1) + self.assertEqual(corpus["scout_runs"][0]["accepted_count"], 1) + + def test_report_includes_scout_usage_summary(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + (root / "data").mkdir(parents=True, exist_ok=True) + (root / "topic.json").write_text( + json.dumps( + { + "topic": "AI theorem proving", + "goal": "Track proof systems", + "audience": "researchers", + "taxonomy": ["proof search", "verification", "benchmarks"], + } + ), + encoding="utf-8", + ) + (root / "data" / "papers.json").write_text( + json.dumps( + { + "papers": [], + "scout_runs": [ + { + "date": "2026-06-19", + "accepted_count": 0, + "accepted_ids": [], + "candidate_count": 7, + "cost": { + "provider": "codex", + "model": "codex-cli:test", + "token_count": 123, + "money_cost_usd": 0.0, + "currency": "USD", + "note": "subscription", + }, + } + ], + } + ), + encoding="utf-8", + ) + env = dict(os.environ, TOPIC_SCOUT_ROOT=directory) + subprocess.run( + [sys.executable, str(ROOT / "scripts" / "build_corpus.py")], + check=True, + env=env, + capture_output=True, + text=True, + ) + report = (root / "reports" / "research_report.md").read_text(encoding="utf-8") + self.assertIn("## Scout Usage", report) + self.assertIn("**Total scout tokens:** 123", report) + self.assertIn("via codex (codex-cli:test)", report) + + def test_scout_llm_api_returns_usage(self): + response_body = { + "output": [ + { + "type": "message", + "content": [ + { + "type": "output_text", + "text": json.dumps( + { + "candidates": [ + { + "id": "openalex:1", + "relevance_score": 8.2, + "relevance_reason": "Matches proof search and verification scope.", + } + ] + } + ), + } + ], + } + ], + "usage": { + "input_tokens": 120, + "output_tokens": 30, + "output_tokens_details": {"reasoning_tokens": 4}, + }, + } + + class FakeResponse: + def __enter__(self): + return self + + def __exit__(self, *args): + return None + + def read(self): + return json.dumps(response_body).encode() + + def fake_urlopen(request, timeout): + return FakeResponse() + + scores, cost = score_candidates_api( + CONFIG, + [ + { + "id": "openalex:1", + "title": "Formal proof search", + "abstract": "Proof search with verifier feedback.", + "topics": ["proof search"], + "relevance_score": 1.0, + "relevance_reason": "heuristic", + } + ], + api_key="test-key", + model="test-model", + urlopen=fake_urlopen, + ) + self.assertEqual(scores["openalex:1"]["relevance_score"], 8.2) + self.assertEqual(cost["token_count"], 154) + self.assertEqual(cost["model"], "test-model") + + def test_scout_llm_codex_parses_usage(self): + captured = {} + + def fake_run(command, **kwargs): + captured["command"] = command + output_path = Path(command[command.index("--output-last-message") + 1]) + output_path.write_text( + json.dumps( + { + "candidates": [ + { + "id": "openalex:1", + "relevance_score": 7.5, + "relevance_reason": "Highly aligned with formal verification scope.", + } + ] + } + ), + encoding="utf-8", + ) + return subprocess.CompletedProcess( + command, + 0, + stdout=( + '{"type":"thread.started"}\n' + '{"type":"turn.completed","usage":{"input_tokens":210,"output_tokens":18,"reasoning_output_tokens":2}}\n' + ), + stderr="", + ) + + with patch("scout_llm.shutil.which", return_value="/usr/local/bin/codex"): + scores, cost = score_candidates_codex( + CONFIG, + [ + { + "id": "openalex:1", + "title": "Formal proof search", + "abstract": "Proof search with verifier feedback.", + "topics": ["proof search"], + "relevance_score": 1.0, + "relevance_reason": "heuristic", + } + ], + cwd=ROOT, + run=fake_run, + ) + self.assertIn("--json", captured["command"]) + self.assertEqual(scores["openalex:1"]["relevance_score"], 7.5) + self.assertEqual(cost["token_count"], 230) + + def test_gap_analysis_skips_when_no_accepted_papers(self): + with tempfile.TemporaryDirectory() as directory: + root = Path(directory) + (root / "data").mkdir(parents=True, exist_ok=True) + papers_path = root / "data" / "papers.json" + opportunities_path = root / "data" / "research_opportunities.json" + papers_path.write_text(json.dumps({"papers": []}), encoding="utf-8") + + def fake_load_json(path, default): + if not path.exists(): + return default + return json.loads(path.read_text(encoding="utf-8")) + + with patch.object(gaps_module, "load_json", side_effect=fake_load_json): + with patch.object(gaps_module, "PAPERS_PATH", papers_path): + with patch.object(gaps_module, "OPPORTUNITIES_PATH", opportunities_path): + with patch.object(sys, "argv", ["analyze_research_gaps.py"]): + self.assertEqual(gaps_module.main(), 0) + self.assertFalse(opportunities_path.exists()) + def test_intent_refinement_can_use_codex_subscription(self): refined = { "title": "Business-Aligned Evaluation of AI Agents",