Vantage Points: Mock Juror Perception of Body-Worn Camera Video Evidence in Cases Involving Police Use of Force
Bibliographic record
Abstract
Recent shooting events have led to demands that police officers use body-worn video cameras (BWCs) in order to increase police accountability.While preliminary research has found that BWCs have some general benefits (e.g., they may decrease use of force and reduce false allegations of police misconduct), no research to date has examined the impact of BWC evidence on juror decision-making.Study 1 in this thesis involves a survey of community members to examine public perception of BWC use.In addition to other findings, the survey results indicate that the majority of respondents do not accurately comprehend the limitations of visual footage and are likely to attribute dishonest intentions to police officers when their testimony contradicts BWC footage (especially when the discrepancies are central versus peripheral in nature).Study 2 involves a mock juror study designed to assess how the degree of discrepancies between officer testimony and BWC footage (few vs. many), the nature of these discrepancies (peripheral vs. central), and the presence of expert testimony designed to explain these discrepancies impacts juror decision-making in a case involving allegations of excessive force by a police officer.The results of this study suggest expert testimony and perceptions of police legitimacy (PL) have a significant impact on verdict decisionmaking (and other outcome variables) in such cases.The implications of these results, for theory and practice, are discussed.
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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 | 0.011 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".