Why have we not detected gender differences in organizational justice perceptions?! An evidenced‐based argument for increasing inclusivity within justice research
Bibliographic record
Abstract
Summary While research from various disciplines shows that women continue to disproportionately face workplace injustices compared to men, OB research has not found meaningful gender differences in self‐reported workplace justice perceptions. This paradox has received little attention in the otherwise well‐established organizational justice literature. We applied an abductive approach to investigate this paradox by a) confirming its existence, and b) proposing and empirically evaluating seven possible explanations for its existence, using multiple methods and seven distinct datasets. We found that this paradox is unlikely to be explained by measurement invariance, different expectations for treatment, whether the context is male‐dominated, differences across years, or differences in how justice perceptions are formed. We did find, however, that when using alternate measurement approaches, women recalled gender‐based injustice experiences, reported them as having occurred more frequently than did men, and reported them as having been negatively impactful on their lives/careers. We conclude that the most promising explanation for this paradox is that extant organizational justice measures are deficient for the purpose of capturing variance accountable to gender‐based injustice. This highlights the need for more inclusive approaches for the measurement and application of organizational justice, especially when studying the relationship between gender and organizational 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 | 0.128 | 0.329 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".