Countering a masculinity contest culture at work: the moderating role of organizational justice
Bibliographic record
Abstract
Purpose The purpose of this study is to test whether organizational justice (i.e. fair treatment) can mitigate the harmful effects of a “masculinity contest culture (MCC)” (i.e. norms, rituals and belief systems valorizing social dominance, work above other parts of life, physical strength and the avoidance of weakness). Design/methodology/approach Through an analysis of secondary survey data collected from a Canadian police organization (N = 488), this study tested the moderating effects of organizational justice on the relationship between employee perceptions of their workplace as a masculinity contest, and a negative outcome variable, harassment. Findings The results of this study suggest that a MCC was significantly related to harassment, which in turn contributed to lower psychological wellbeing and increased turnover intentions. Independently, organizational justice moderated the effect of a MCC on harassment, suggesting that harassment is less prevalent in the workplace when there is a greater focus on treating all employees fairly. Originality/value Despite the increase in both scholarly and practitioner interest in the effects of organizational cultures in which employees seek to maintain their own status at all costs, there has been little research examining the interactions of these harmful workplace cultures and the factors that might counter them. To the best of the author's knowledge, this is one of the rare studies to investigate possible interventions for harmful workplace cultures.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".