Use of Force by Male and Female Officers: Rates, Modalities, Effectiveness, and Injuries
Bibliographic record
Abstract
This brief is based on the following published work:Sheppard, J., Khanizadeh, A.-J., Baldwin, S., & Bennell, C. (2024). A comparison of police use of force by male and female officers in Canada: Rates, modalities, effectiveness, and injuries. Criminal Justice and Behavior, 51(5), 743-767. https://doi.org/10.1177/00938548241227551 The source article examined sex differences in: (1) use-of-force rates, (2) the use of different intervention options, (3) the effectiveness of different intervention options, and (4) subject and officer injury rates. Female officers were less prone to using force and prefer techniques requiring less physical strength (e.g., intermediate weapons), resulting in fewer injuries to suspects but a higher likelihood of sustaining injuries themselves. Police services may want to consider increased training in control techniques, like those used in jiujitsu, which are effective regardless of someone’s strength or build. There is a benefit associated with intermediate weapons, such as conducted energy weapons (CEWs), in achieving a balance between effectiveness and injury rates. Police services should equip officers with intermediate weapons and ensure officers receive proper training on their use.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".