A Critical Review of Algorithms in HRM: Definition, Theory, and Practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The recent surge of interest concerning data analytics in both business and academia has been accompanied by significant advances in the commercialization of HRM (Human Resource Management)-related algorithmic applications. Our literature search uncovered 22 high-quality academic papers and 122 practitioner-oriented items (e.g., popular press and trade journals). As part of our review, we draw several distinctions between the typical use of HRM algorithms and more traditional statistical applications. We find that while HRM algorithmic applications tend not to be especially theory-driven, the “black box” label often invoked by critics of these efforts is not entirely appropriate. Instead, HRM-related algorithms are best characterized as heuristics. In considering the implications of our findings, we note that there is already evidence of a research-practitioner divide; relative to scholarly efforts, practitioner interest in HRM algorithms has grown exponentially in recent years.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it