Juror Decision-Making in a Case Involving Police Use of Force: The Impact of Defendant Race, Victim Race, and Victim Gender on Verdict and Perceptions
Bibliographic record
Abstract
In recent years, cases of police use of force have been making headlines and sparked a global conversation about racial bias in policing. As such, the current study aimed to uncover how race and gender impact juror decision-making in a case involving mistaken identity by police. We hypothesized that mock-jurors would be more likely to assign guilty verdicts and have a more negative perception of the defendant when the defendant is White versus Indigenous, and when the victim is White and female. Mock-jurors (N = 229) read one of eight fictional trial transcripts, provided a dichotomous verdict, and responded to questions regarding their perceptions of the defendant and the victim. While there was no significant impact on the dichotomous verdict or continuous guilt, the mock-jurors had a significantly more favourable perception of the Indigenous defendant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".