The effect of victim race on jurors’ perceptions of lethal use of force
Bibliographic record
Abstract
In recent years, a number of highly-publicized lethal police use of force (UoF) encounters have occurred in both Canada and the United States, sparking several social movements and causing public debate about officer accountability. The primary aim of this project is to increase our understanding of jurors' legal decision-making in trials involving police UoF by exploring what jurors discuss during deliberations in simulated trials and evaluating 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. The victim's race was manipulated to be either White or Indigenous. After rendering individual pre-deliberation verdicts, participants took part in a 60-minute deliberation session, then rendered individual post-deliberation verdicts. Study 1a investigated the relationship between victim race, jurors' perceived police legitimacy, and individual verdict decisions. 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 more than 16 times higher when the victim was White as opposed to Indigenous. Study 1b investigated how victim race and police legitimacy relate to the deliberation content of the juries. Analyses indicated that both of these variables play a significant role in jury deliberations. Specifically, jurors were significantly more likely to provide "anti-defendant" and "pro-prosecution" utterances when the victim was White, as compared to Indigenous. 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. eight times." Three of the bullets hit the man, resulting in his death. Authorities later identified the victim as 30-year old Ojibwe man Greg Ritchie. Ritchie's family say "he suffered from mental illness," (CBC, 2019) and that he was on his way to pick up medication on the day that he was killed. In their investigation of the incident, the SIU concluded that the officers had used reasonable force in their decision to shoot Ritchie, and recommended no charges be filed. Following Ritchie's death, a number of demonstrations and protests occurred. One of the organizers of these demonstrations, Jocely Wabano-Iahtail, said "We've been here numerous times with the loss of life of other brothers and sisters. Whether it's from our community or the Black community, we've been here over and over again. And this needs to stop" (CBC, 2019).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".