Educating the Jury: Mental Illness and Criminal Responsibility in the Canadian Courtroom
Bibliographic record
Abstract
This study explored juror stigma towards defendants with mental illness (MI), and examined the impact of education on juror decision-making in Not Criminally Responsible on account of Mental Disorder (NCRMD) cases in Canada.Four-hundred and eighty-six participants received two forms of education (MI or diabetes, and NCRMD or duress), read an NCRMD trial transcript in which the defendant's MI was manipulated, and provided a verdict (NCRMD or guilty), defendant perceptions, MI attitudes, and NCRMD attitudes.Continuous analyses revealed that MI and NCRMD education did not have a combined effect on defendant perceptions, MI stigma, or decision-making in NCRMD cases.Path analysis revealed that MI type was directly and indirectly related to guilt certainty via MI attitudes and defendant perceptions.However, NCRMD attitudes did not have an effect on verdicts.Results imply that MI attitudes are deeply ingrained in social and cultural norms and are not amenable to change despite education. Educating the Jury: Mental Illness and Criminal Responsibility in the Canadian CourtroomMental illness directly or indirectly affects all Canadians, regardless of age, religion, culture, socioeconomic status, or education level.Approximately 20% of Canadians will personally experience a mental illness during their lifetime (Health Canada, 2002).Although the majority of persons with mental illness do not come into contact with the criminal justice system, Canada's correctional facilities have seen a considerable increase in the number of offenders experiencing mental health problems upon admission (Correctional Service of Canada, 2009).This increase could be due to the closing of civil psychiatric facilities or better measurement for the detection of mental illness (Prins, 2011;Sheth, 2009;Stall, 2013).Furthermore, when compared with the general population, offenders have a higher prevalence rate of mental illness (Canadian Institute for Health Information, 2008; Correctional Service of Canada, 2009).However, this high prevalence rate could be due to the fact that mentally ill people are less adept at committing crime, more likely to get arrested due to their behaviour, and are more likely to plead guilty (Bland, Newman, Dyck, & Orn, 1990; Canadian Mental Health Association, 2004;Teplin, 1984).In 2010-2011, Correctional Service Canada reported that 62% of offenders entering a federal penitentiary had been identified for a follow-up mental health assessment or service (Office of the Correctional Investigator, 2012).In addition, approximately 4 out of 5 offenders in federal custody are afflicted by more than one mental illness, most commonly substance use disorder (Office of the Correctional Investigator, 2012).Although initiatives have been established to provide appropriate services and interventions that will assist mentally ill offenders, these offenders pose a
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".