The Role of Defendant Race and Racially Charged Media in Canadian Mock Juror Decision Making
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
This study examined the influence of defendant race and race salience (manipulated via racially charged media) on Canadian mock jurors' judgements. Two hundred ten jury-eligible Canadian online participants read a racially charged (general or specific to the defendant's race) or neutral article followed by a trial transcript that involved dangerous operation of a motor vehicle and impaired driving charges against a White, Black, or Indigenous defendant. Diverging from previous findings, this study did not find effects of defendant race or race salience on verdict judgements or causal attributions. However, when race is not a central feature of the case, making race salient outside the trial may increase levels of racial bias for some mock jurors. When the defendant was Black, a race-specific article appeared to backfire, producing the harshest sentencing recommendation compared to race-neutral and general race articles. Conversely, for the Indigenous defendant, any mention of race produced harsher recommended sentences relative to no mention of race. Results do not seem to parallel those found in U.S. race-salience studies. Rather, this specific race-salience technique may be detrimental to a minority defendant's case in a Canadian context.
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 | 0.005 | 0.029 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| 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