The fairness of human resource management practices: an assessment by the justice sensitive
Bibliographic record
Abstract
Introduction: Although fairness is a pervasive and ongoing concern in organizations, the fairness of human resource management practices is often overlooked. This study examines how individual differences in justice sensitivity influence the extent to which human resource management practices are perceived to convey principles of organizational justice. Methods: Analysis was performed on a matching sample of 283 university students from three academic units in two countries having responded at two time points. Justice sensitivity was measured with the 40-item inventory developed and validated by Schmitt et al. (2010). Respondents were instructed to indicate to what extent each of 61 human resource management practices generally conveys principles of organizational justice. Results: Justice sensitivity was positively associated with subsequent assessments of the justice contents of human resource management practices. The distinction between self-oriented and other-oriented justice sensitivities was helpful in determining perceptions of these human resource management practices and of a subset of pay-for-performance practices. Discussion: The findings inform current research about the meanings borne by human resource management practices, and also increase understanding of entity judgment formation as an important aspect of systemic justice.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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; 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".