Addressing barriers to promotion for female officers in municipal policing in Canada
Bibliographic record
Abstract
Female police officers face numerous barriers to promotion in municipal policing in Canada. For a variety of reasons, including that policing remains male-dominated, there are very few female officers in higher ranking positions in municipal police agencies in Canada. With few female leaders comes a lack of female role models, mentors, and the female influence in decision making at executive levels. This has had a broad effect on how police agencies respond to the communities they serve. This research summarized the value women bring to the policing profession and explored the barriers to promotion identified in the literature that have affected the number of women in senior leadership roles in Canada. The empirical data collected in this study is derived from surveys completed by 413 police officers working in several municipal police organizations in Canada. The intent of the survey was to explore the real and perceived barriers female officers experience in promotion to higher ranks. The results of the survey offered some important findings, including that barriers to promotion for women still exist in Canadian municipal policing. Recommendations are made suggesting ways that police leaders could use the information derived from this research to address real and perceived barriers to promotion for female officers in their respective policing agencies.
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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 | 0.002 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".