Masculinity Contest Cultures in Policing Organizations and Recommendations for Training Interventions
Bibliographic record
Abstract
Abstract In the wake of the #BlackLivesMatter and #MeToo movements, police conduct has been increasingly scrutinized by the public, especially the use of excessive force, fatal shootings of unarmed civilians, and sexual harassment scandals within policing organizations. Through a review of the policing literature and data collected in a Canadian policing organization, we highlight how masculinity contest culture is related to police misconduct. All four masculinity contest culture dimensions can be observed in policing including: (1) “show no weakness,” (2) “strength and stamina,” (3) “put work first,” and (4) “dog‐eat‐dog.” Masculinity contest cultures lead to negative outcomes for both individual officers (e.g., harassment, discrimination, stress), policing organizations (e.g., lawsuits, turnover), and communities (e.g., officers’ use of excessive force). Training interventions are often suggested to prevent or remedy the negative effects of masculinity contest cultures in policing organizations. However, a review of the training literature suggests that training interventions are unlikely to be effective in contexts where organizational norms are at odds with the training content. Our analysis of police data, along with the literature review, conclude with a paradox—the very organizations that need training interventions the most (e.g., policing organizations that often promote and tolerate sexual harassment) are the least likely to benefit from those interventions. To address this paradox, we invoke the theory of social interactionism and reconceptualize training as an organizational sensegiving mechanism. This theoretical foundation offers new directions for future research on training in masculinity contest cultures and insights for practicing police administrators and public policy officials.
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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 | 0.028 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".