Body-worn camera videos and public perceptions of police: an experiment on positive video exposure and community-police relations
Bibliographic record
Abstract
Objective Explore whether exposure to positive BWC videos – particularly, acts of heroism – affects public perceptions of police while accounting for existing trust in police.Method An online vignette-style experiment was conducted in which participants’ (N = 407; x¯age = 41.94 [s = 12.74]; 51% women, 80% White) existing trust in police was measured before random assignment to either view a short series of positive BWC videos or not. All participants then read a vignette describing a traffic stop. Participants reported their perceptions of procedural and distributive justice as well as perceptions of police more generally.Results Participants with higher existing trust in police reported more positive evaluations across all outcomes measured. Exposure to positive BWC videos only increased reported willingness to cooperate with police. However, trust in police and exposure to BWC videos produced an interaction effect: when participants’ existing trust in police was low, viewing positive BWC videos improved evaluations of officer respect and procedural justice as well as willingness to cooperate with police. Participants with low trust in police who viewed the positive videos became more similar to participants with high existing trust in police.Conclusion The findings indicate that exposure to positive BWC videos can moderate the negative effect of low trust in police. In an applied sense, the results suggest that police-community relations may be enhanced by circulating videos that depict acts of police heroism when such events have occurred and are captured on film.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".