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Record W4392057815 · doi:10.1177/00938548241227551

A Comparison of Police Use of Force by Male and Female Officers in Canada: Rates, Modalities, Effectiveness, and Injuries

2024· article· en· W4392057815 on OpenAlexaffabout
J. T. Sheppard, Ariane‐Jade Khanizadeh, Simon Baldwin, Craig Bennell

Bibliographic record

VenueCriminal Justice and Behavior · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsCarleton University
Fundersnot available
KeywordsModalitiesPoison controlHuman factors and ergonomicsInjury preventionOccupational safety and healthEngineeringMedical emergencySuicide preventionForensic engineeringMedicinePhysical therapyPsychologySociology

Abstract

fetched live from OpenAlex

Research has reported inconsistent findings with respect to how female and male police officers use force. This study examined this issue in a Canadian context. Use of force data over 9 years were collected from a large Canadian police agency. The results demonstrated that, overall, female officers used force less frequently than male officers relative to the number of female and male officers within the participating police agency. Female officers had lower odds of using physical control “hard” options (e.g., stuns and strikes) and higher odds of using intermediate weapons (e.g., conducted energy weapon) compared with male officers. Female officers also generally reported less effectiveness, more injuries to themselves, and fewer injuries to subjects related to their use of force compared with male officers. Literature on police use of force and social role theory are used to help explain the findings, and recommendations for improving outcomes in police–public interactions are suggested.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.098
GPT teacher head0.410
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2024
Admission routes2
Has abstractyes

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