“Suck It Up, Buttercup”: Understanding and Overcoming Gender Disparities in Policing
Bibliographic record
Abstract
Women police officers report elevated symptoms of mental disorders when compared to men police officers. Researchers have indicated that the occupational experience of policing differs greatly among men and women. Indeed, police culture is characterized by hegemonic masculinity, which appears to negatively impact both men and women. The current study examined the contrast between the experiences of men and women police officers. Police officers (n = 17; 9 women) in Saskatchewan participated in semi-structured interviews. Thematic network analysis identified themes related to the experience of policing for both men and women police officers. There were six organizing themes identified in relation to the global theme of Gendered Experiences: (1) Discrimination; (2) Sexual Harassment; (3) Motherhood and Parental Leave; (4) Identity; (5) Stereotypically Feminine Attributes; and (6) Hegemonic Masculinity. Pervasive gender norms appear detrimental for both men and women police officers, as well as the communities they serve. The current results, coupled with the emerging disposition for progress expressed by police services, offer opportunities to develop tailored and focused interventions and policies to support police officers.
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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 | 0.005 | 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 | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".