A Commentary on Miron et al. (2024): Why Managers May Not Actually Stop Caring About Gender Inequality
Bibliographic record
Abstract
Drawing upon the Motivated Perceptual Approach (MPA; Bashshur et al., 2023), we challenge Miron et al.'s (2024) overall conclusion that managers "stop caring about organizational justice".We first analyze Miron et al. (2024)to critique the overgeneralization of the scholarly assumptions and conceptualizations and also raise concerns about the ecological validity of the findings.We then showcase how applying the MPA to the questions raised by Miron et al. (2024) can enhance theoretical and practical insights to advance gender equity, including providing actionable practical strategies people can use to bring inequality to others' attention.Taken together, we encourage scholars and practitioners to share information that can prompt others to perceive and respond to inequality as well as enhance equality in organizations.Given persistent gender inequalities in the workplace, Miron et al.
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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 | 0.022 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.098 | 0.096 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".