Increasing the number of policewomen through job advertisements and recruitment methods
Bibliographic record
Abstract
How to increase diversity within policing is a question that has received substantial political and media attention in recent years. The ideal police applicant and the qualities sought have transformed alongside social changes, shifts in police work, and cultural variations. While many of these changes have created more opportunities for women, females remain underrepresented in the Royal Canadian Mounted Police (RCMP). As such, it is important to examine ways police organizations in Canada can entice more females to apply. One area of particular interest is the recruitment process, specifically the recruitment material developed. By exploring the existing literature and utilizing several theoretical models, this preliminary study describes the value of diversity within the professional context of policing, identifies how policies and recruitment methods have changed and outcomes of these changes, and explains how job postings may contribute to keeping the number of women low in the field of policing. Based on this assessment, practical recommendations are provided to develop effective advertisements to increase the number of women who apply to the RCMP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.567 | 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 teacher head, 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".