The effects of body‐worn camera footage and eyewitness race on jurors' perceptions of police use of force
Bibliographic record
Abstract
Police use of body-worn cameras (BWCs) is increasingly common in the USA. This article reports the results of one of the first experimental examinations of the effects of three BWC status conditions (absent, transcribed, viewed) and eyewitness race (Black, White) on mock jurors' case judgments, in a case in which a community member (defendant) was charged with resisting arrest but where the officer's use of force in conducting the arrest was controversial. Results provide evidence of significant main effects of both eyewitness race and BWC status. When the eyewitness supporting the defendant was White, mock jurors were less likely to vote the defendant guilty of resisting arrest, as well as more likely to consider the defendant credible and the officer culpable for the incident. In addition, when BWC footage of the arrest was viewed, compared with transcribed or absent, participants were less likely to vote the defendant guilty of resisting arrest, and also rated the officer's use of force less justifiable, and the officer more culpable and less credible. Follow-up analyses demonstrated that these relationships between BWC condition and case judgments were all mediated by moral outrage toward the officer.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".