Critical Review of Visual Models for Police Use of Force Decision-Making
Bibliographic record
Abstract
Recent calls for widespread police reform include re-examination of existing training and practice surrounding the use of force (UOF, e.g., verbal and non-verbal communication, physical tactics, firearms). Visual models representing police UOF decision-making are used for both police training and public communication. However, most models have not been empirically developed or assessed in either the applied police or vision science literatures, representing significant gaps in knowledge. The purpose of the current review is to provide a novel, relevant, and practical analysis of the visual components of three common police UOF decision-making model types (circular, cyclical, staircase). We begin with a critical evaluation of the visual features specific to each model type (i.e., shape), followed by critical reviews of common visual features, including colour, implied motion, text, and clarity. The insights provided by the current work afford scientists from visual disciplines a unique opportunity to contribute meaningfully to the improvement of existing police UOF practices, with the goal of promoting public and occupational safety. To this end, we conclude with evidence-based recommendations for designing visual models that effectively promote training of police and communication of police UOF decision-making to the public.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".