Considering Objective and Subjective Measures for Police Use of Force Evaluation
Bibliographic record
Abstract
In spite of significant interest in the application of police use of force (UOF) from organisations, researchers, and the general public, there remains no industry standard for how police UOF is trained, and by extension, evaluated. While certain UOF behaviours can be objectively measured (e.g., correct shoot/no shoot decision making (DM), shot accuracy), the subjective evaluation of many UOF skills (e.g., situation awareness, SA) falls to the discretion of individual instructors. The aim of the current brief communication is to consider the operationalisation of essential UOF behaviours as objective and subjective measures, respectively. Using longitudinal data from a sample of Canadian police officers (n = 57) evaluated during UOF training scenarios, we discuss how objective and subjective measures reflect changes in officer performance over time. Objective lethal force DM was measured as a binary ‘correct–incorrect’ outcome and subjective SA was measured on a 5-point Likert scale ranging from ‘unacceptable’ to ‘exceptional’. Subjective evaluation of SA demonstrated significant changes over time, while DM remained relatively high and stable. Given the practical and professional implications of UOF, we recommend that a combination of objective and subjective measures is systematically implemented at all stages of police UOF training and evaluation (i.e., basic, advanced, in-service).
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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 | 0.089 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".