Social Identity in the Canadian Courtroom: Effects of Juror and Defendant Race
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this study was to examine whether black (n = 90), Indigenous (n = 92), and white (n = 94) mock jurors would make harsher decisions in trials involving other-race defendants. Jury-eligible community members recruited via Qualtrics read a fictional impaired driving and dangerous operation of a motor vehicle case in which the defendant’s race varied (black, Indigenous, white). They then made verdict/sentencing decisions and completed measures of stereotypes. We predicted that mock jurors who endorsed negative racial stereotypes would be more likely to vote guilty and recommend harsher sentences for other-race defendants. Instead, we found that positive personally held stereotypes predicted leniency among white jurors judging Indigenous defendants but no such effects for other trial party combinations. Overall, the black defendant received significantly more lenient decisions as compared to the white defendant. Although no formal policy ensures that specific groups are represented on juries, these data indicate that people process trial information differently as a joint function of juror and defendant race.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it