Terror in the Justice System: Effects of Defendant Race and Religion on Juror Decision-Making in a Criminal Trial
Bibliographic record
Abstract
New Canadian anti-terror legislation was passed in 2015, expanding the scope of criminal offences to include advocating or promoting terrorism offences in general.This study explored juror perceptions of the applicability of this law by having participants read a trial transcript involving this charge in which the defendant's race (Black/White/Arab) and religion (Christian/Muslim/undisclosed) were manipulated.Participants provided a guilty/not guilty verdict, then answered a brief questionnaire on attributions of the defendant's actions and stereotypes held by the Canadian public.Results demonstrated that two attribution measures, defendant stability and defendant responsibility, were related to verdict outcome.Of note, at middling levels of defendant responsibility, the defendant's religion influenced verdict outcome, leading to more guilty verdicts for Muslim defendants.Furthermore, although defendant religion only showed a weak effect on verdict outcome, results indicated that this might operate via stereotypes of the defendant's religious group.Additionally, at some levels defendant stability and defendant responsibility were related to the strength of the effect produced by stereotypes of the defendant's religious group.Although White Canadians received lower stereotype ratings than Black or Arab Canadians, White defendants received more internal ratings of attribution than either Black or Arab defendants.Muslim Canadians received higher stereotype ratings than Christian Canadians and Canadians with no disclosed religion, and Muslim defendants' actions were perceived as less stable than Christian defendants or defendants with no disclosed religion.Finally, there was no direct effect of defendant race on verdicts.While no significant effect was found of racial bias, the results demonstrate important biases that may influence juror decisionmaking in anti-terrorism trials.Most deservedly, I will begin with a heartfelt thank you to Dr. Evelyn Maeder.The success of this project has relied utterly on her support, assistance, and amazing patience.She has been nothing short of a phenomenal presence through my years at Carleton University.I would also like to thank Dr. Craig LethSteensen and Dr. Andrew Smith, whose assistance in plotting the statistical analyses present in this project was exemplary.Lastly, I would like to thank each and every member of the Legal Decision-Making Lab, who have taught me much in our meetings, and whose company made the journey all the better.
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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 | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".