Let’s (not) talk about race: comparing mock jurors’ verdicts and deliberation content in a case of lethal police use of force with a White or Indigenous victim
Bibliographic record
Abstract
Several lethal police use of force (UoF) encounters have recently occurred across North America, sparking public debate about officer accountability. This project investigated what jurors discuss during deliberations in simulated trials involving UoF and evaluated whether the race of the victim affects individual verdicts and deliberation content. Canadian jury-eligible participants (N = 78) watched and listened to a fictional trial involving a police officer charged with manslaughter with a White or Indigenous victim. After rendering individual pre-deliberation verdicts, mock jurors took part in a 60-minute deliberation session, then rendered individual post-deliberation verdicts. Although victim race did not have a statistically significant effect on pre-deliberation verdicts, the odds of jurors rendering a guilty post-deliberation verdict was nearly 10 times higher when the victim was White as opposed to Indigenous. Deliberation analyses indicated that jurors were significantly more likely to provide ‘anti-defendant’ and ‘pro-prosecution’ utterances when the victim was White as compared to Indigenous. However, jurors very rarely directly discussed race in deliberations. Additionally, jurors with negative perceptions of police were significantly more likely to utter ‘anti-defendant’ statements. Overall, this study suggests that, contrary to the assumption of the Canadian legal system, victim race influences legal decision-making in trials involving officer UoF.
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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 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".