Perceptions of Police Use of Force: The Influence of Inconsistencies, Victim Race, Defendant Race, and Situation Type on Mock-Juror Decision-Making
Bibliographic record
Abstract
The current set of studies examined whether various factors influenced mock-juror decision-making for a use of force case with a police officer defendant. Study 1 examined the effect of the number of inconsistencies (3 vs. 9), victim race (White vs. Indigenous), and defendant race (White vs. Indigenous). Results showed that a higher number of inconsistencies and a White defendant elicited less favourable perceptions of the defendant and higher perceptions of guilt. Study 2 examined the effect of the type of emergency situation (mental health check vs. domestic violence), victim race (White vs. Indigenous), and defendant race (White vs. Indigenous). Results showed that a mental health check situation, an Indigenous victim, and a White defendant elicited less favourable perceptions of the defendant and higher perceptions of defendant guilt. Participant attitudes were also examined and found to be influential on decision-making. Implications of the findings and directions for future research 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 | 0.012 | 0.066 |
| 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 | 0.002 | 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".