Bibliographic record
Abstract
This paper offers an overview of the usefulness of bibliographic analysis and conducts a retrospective review of the Human Resource Management Journal (HRMJ). The first part of the paper explains that bibliographic methods are not fully democratized in management and have just started their modest ascent in HRM. In the second part, the paper provides a retrospective review (1990–2022) of HRMJ articles (n = 855), which constitutes one of the two general objectives of bibliometric analysis. The results revealed that bibliometrics analysis significantly contributes to the HRM body of knowledge, especially given that this methodology is barely used in HRM research. Conversely, the retrospective review has identified trends in performance analysis, such as co-authorship internationalization in HRMJ articles that were unclear until 2011. We also highlighted the existence of six major clusters: HRM and organizational behavior (cluster 1), HR practices and policies (cluster 2), HRM as a strategy (cluster 3), HRM and performance (cluster 4), international HRM (cluster 5), and HRM roles and contingencies (cluster 6). Insights into potential trajectories for HRMJ scope are provided.
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 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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.011 | 0.049 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".