Indigenous Victims with Mental Illness: The Influence of Stereotypes and Stigma on Mock-Juror Perceptions
Bibliographic record
Abstract
This study examined jurors' perceptions of victim race and mental illness as a function of defendant race in a mock-juror decision-making context.Victim race (Indigenous vs. White) and Mental Illness (substance abuse vs. schizophrenia vs. no mental illness) along with officer's race (Indigenous vs. White) were manipulated in a mock-trial transcript.Participants (N = 320) were recruited from psychology courses.Contrary to the hypotheses, Indigenous victims and defendants were perceived more positively than White victims or defendants.In line with the hypotheses, victims with substance use disorder and schizophrenia were perceived as less credible than victims with no mental illness, that was exacerbated by Negative Beliefs Toward Mental Illness.Defendants were less likely to be convicted when the victim was Indigenous with a mental illness.This research contributes to the understanding of juror bias against marginalized communities and can be used to inform policies in the Criminal Justice System towards promoting more just practices.thesis.Firstly, I owe so much gratitude to Dr. Joanna Pozzulo.Not only did she play an important role in the completion of thesis, but she taught me so much and provided me with many incredible opportunities that have made a huge positive impact on my academic career.She took a chance on me, and I appreciate the amount of kindness, time, expertise, and guidance she provided me.I would also like to thank my secondary supervisor, Dr. Emily Pica for meeting with me countless times, answering all my questions without judgement, and for always being a positive and supportive mentor to me.I would like to also thank the PhD students in my lab, Lauren Thompson, and Bailey Fraser.They have supported me through the completion of this thesis and were always there to answer questions or meet with me when needed.I would like to thank my fellow MA students in my lab -Alexia
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".