Masculinity contest cultures and organizational outcomes in police organizations
Bibliographic record
Abstract
Abstract As a male-dominated occupation that has historically valued strength, risk-taking, and control, policing may be particularly susceptible to masculinity contest cultures (MCC), characterized by social norms that valorize physical ability, avoid weakness, prioritize work, and promote dominance. Through surveys of patrol officers from five Canadian police agencies (n = 238), this study explored the existence of MCCs and their relationship with organizational outcomes. The study suggested that an officer’s perception of their work within an MCC can predict job engagement (b = −0.48, P < .001), work meaning (b = −0.35, P < .001), self-reported performance (b = −0.07, P < .001), and turnover intention (b = 0.07, P < .001) when controlling for gender, supervisory status, years of police service, and university education. Additionally, university degrees were a significant positive predictor of MCC scores (b = 2.40, P < .01), indicating that university-educated police officers perceive their workplace cultures as more masculine. As the first study that related MCCs to university education, job engagement, and work meaning in policing, it advances our understanding and provides insights into how these cultures relate to organizational outcomes. The results also have implications for policy and police administration.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".