Crazy and Caucasian? The Influence of Race, Gender, and Crime Variables on Perceptions of Criminal Responsibility
Bibliographic record
Abstract
Past research has shown that the way people perceive a crime depends on a variety of extralegal factors, especially when determinations of Not Criminally Responsible on Account of Mental Disorder (NCRMD) are at stake. In particular, the Canadian criminal justice system has a demonstrated bias against people of colour in relation to guilty verdicts and punitiveness. Further, mental illness is often interpreted differentially in light of the gender of the perpetrator and their criminal history. As such, this study was designed to assess how participants interpret a case of homicide where the following variables have been manipulated: (1) perpetrator race (Indigenous, Black, Caucasian), (2) perpetrator gender (man or woman), (3) perpetrator-victim relationship (stranger v. known), and (4) criminal history (none, NCR, CR, NCR+CR). Participants will be presented with crime and trial summaries, and information pertaining to the defendant’s mental health (i.e., diagnosis of schizophrenia with ambiguous influence on the commission of the crime). They will also complete several measures of bias and a judgment questionnaire. We anticipate that defendants of colour and men will be more likely to be deemed criminally responsible and sentenced more harshly, however previous NCR or CR determinations will sway prospective jurors’ views in the same direction as the information provided. Further, we predict that NCR designations will be more common when the victim is a stranger. This study has important implications for judicial bias and how extralegal factors continue to exert large influences on our judgments. Department: Psychology Faculty Mentor: Dr. Kristine Peace
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".