AN INVISIBLE NETWORK OF KNOWLEDGE PRODUCTION: 10 YEARS OF HUMAN RESOURCES MANAGEMENT STUDIES
Bibliographic record
Abstract
To map the intellectual structure of human resources management studies in the past decade, this study proposes a generic model of invisible network of knowledge through which the most important publications and the most influential scholars as well as the correlations among these publications can be identified. Then using 33,132 citations of 1,267 articles published in SSCI journals in human resources management area between 1996 and 2005, this study maps an invisible network of knowledge of human resources management studies. The past decade has seen active research in human resources management and thus produced an impressive array of literature in human resources management studies. While research findings in human resources management can be disseminated to scientists and practitioners in the form of journal articles, papers, books, and other documents, people are easily confused with the subjects and their contributions to the development of human resources management when faced with hundreds of such publications. Great efforts have been made to explore these issues, yet all the issues are usually discussed solely based on the subjective assessment of different experts, which often leads to many controversies in the human resources management area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.026 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".