Self‐induced extreme intoxication akin to automatism: A psycholegal tug of war
Bibliographic record
Abstract
Self-induced extreme intoxication akin to automatism (SIEA) is a complicated and controversial legal concept resistant to jurisdictional consensus. In the United States, SIEA has, at times, been considered under the concept of "settled insanity.". In the United Kingdom, the defense may be allowed for specific intent crimes, though the defendant's awareness of the foreseeability of risk is addressed at trial. In Canada, recent jurisprudence has led to legal and practice landscape changes related to self-induced extreme intoxication. Here, we provide an overview of automatism and an update on the Canadian perspective with a review of the facts and an analysis of the Supreme Court of Canada's landmark decision in R v. Brown, where the court permitted the SIEA defense to be utilized for general intent crimes and acquitted Matthew Winston Brown, a 26-year-old male with no history of mental illness, with respect to two counts of "break and enter" and one count of "aggravated assault." We review the social and legislative response to the changing case law as well as related implications for expert testimony, which may be provided by forensic mental health professionals. Given the judicial and legal implications of the recent changes for both perpetrators and victims of violent crime and given the dynamic international landscape on extreme intoxication in criminal law, the review is thought to be of interest to a broad category of stakeholders including policymakers and those working in forensic psychiatry and law.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".