The RIDE study: Effects of body‐worn cameras on public perceptions of police interactions
Bibliographic record
Abstract
Research Summary During a brief interaction with motorists (i.e., a sobriety check), this study manipulated officer use (and declaration) of a body‐worn camera (BWC) (present; absent) while documenting participant BWC recollection (correct; incorrect) to assess effects on motorists’ perceptions of the encounter and of police more generally. Results (N = 361) demonstrate that perceptions of procedural justice were more favourable in the BWC‐present condition when the entire sample was included in the analyses, but that this effect was not significant when focusing on the subset of the sample that correctly recollected BWC use (though the pattern of the effect was the same in both analyses). Policy Implications In combination with results from a handful of similar studies, this study's results suggest that BWCs may be a tool that can be leveraged to enhance public perceptions of encounters with police; however, more research is needed to substantiate this claim. In particular, the development of evidence‐based policy on this matter necessitates continued studies that address issues such as sample imbalances (e.g., gender and minority status), length of the interaction studied (i.e., experimental dosage), and controlling for officer behavior.
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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 | 0.029 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".