MOCK JUROR RATINGS OF GUILT IN CANADA: MODERN RACISM AND ETHNIC HERITAGE
Bibliographic record
Abstract
This research investigated whether the prejudicial attitudes of mock jurors in Canada produce criminal sanction disparities similar to those reported by research in the United States. In order to investigate this hypothesis, English Canadian participants read a transcript of a sexual assault trial that varied the ethnic background of both the victim and the defendant (i.e., English, French or Native Canadian). Participants were then asked to rate the guilt of the defendant in two ways: (1) on a 7-point bipolar scale in accordance with their personal beliefs (i.e., Subjective Guilt Rating), and (2) on a dichotomous scale (guilty/not guilty) in accor- dance with judicial instructions (i.e., Legal Standard Guilt Rating). Participants were also asked to rate the victim and defendant on a number of personality traits. Results indicate that participants asked to rate the degree of guilt of the defendant according to the Subjective Guilt Rating found him more guilty if he was French, or Native Canadian as opposed to English Canadian. These prejudicial ratings, however, dissipated when participants were asked to rate the guilt of the defendant according to the Legal Standard Guilt Rating that included jury instructions. This apparent paradox in results is discussed in terms of modern racism theory.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".