Guest editorial: Artificial intelligence (AI) in the world of work: bibliometric insights and mapping opportunities and challenges
Bibliographic record
Abstract
Purpose This editorial review presents a bibliometric account of the convergence of the fields of artificial intelligence (AI) and human resource management (HRM) and an overview of the related contributions in this special issue. It also explores the expansive area where research on AI and HRM intersects, a domain experiencing rapid growth and transformation, faster than we envisaged. Design/methodology/approach This substantive editorial employs a range of bibliometric analytical tools to present a state of knowledge on the topic and also provides an analytical overview of the contributions in this Special Issue. Findings A thorough examination of scholarly publications spanning two decades illuminates the evolutionary path of themes, key contributors, seminal works and emerging trends within this interdisciplinary sphere. Leveraging co-word analysis, we distill essential themes and insights from an extensive dataset of 654 journal publications curated from the Web of Science database. Our analysis underscores critical research domains, highlighting the nuanced interplay between HRM and AI. Originality/value By integrating findings from the bibliometric analysis and the contributions from the papers in the Special Issue, we highlight and speculate where the field is heading and where scholars have crucial? Opportunities to contribute to going forward.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".