Motivational Influences in Persons Found Not Criminally Responsible on Account of Mental Disorder: A Review of Legislation and Research
Bibliographic record
Abstract
This paper provides a review of the legislative reforms and case law that have impacted the defense of Not Criminally Responsible on Account of Mental Disorder (NCRMD) in Canada over the past three decades. As in other jurisdictions internationally, we observe that legislative reforms of procedural, as opposed to substantive, aspects of the NCRMD defense have impacted the manner in which NCRMD criteria are applied in common practice. More people are being declared NCRMD in recent years, and there is greater heterogeneity in the offending and psychiatric profiles of these individuals, suggesting that NCRMD criteria are being applied more liberally over time. In light of the substantial growth of the forensic mental health system over the past two decades, witnessed both in Canada and abroad, we propose that the study of motivational influences underlying the offending behaviors of persons with serious mental illness (SMI) is necessary to begin disentangling symptom-based offending from violent and antisocial behaviors that may have other motives. This, in turn, can help to determine legal issues, better define the nature of each person's offending and treatment needs, and provide a more fine-grained analysis of the drivers behind the growth experienced by the forensic system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".