The Presumption of Sanity, Automatism and R. v. H. (S.): Is it Insane to Have a Presumption of Insanity?
Bibliographic record
Abstract
This article will attempt to trace the history of the defence of automatism to understand the radical transformation regarding the presumption of sanity in the last several years. In particular, the definition and historical perspectives of automatism will be discussed. The traditional burden of proof and a comparison of mental disorder and non-mental disorder automatism will then be explained. Then the history of the insanity defence will be examined with reference to the presumption of sanity. The cases of R. v. Rabey and R.v.Parks will be discussed with a focusonthe continuing danger theory, and internal and external divisions within the defence of automatism. Then the case of Stone and the very recent Ontario Court of Appeal decision in R. v. H. (S.) will be critically examined. Focusing on the latest case from the court that there should be a presumption of mental disorder on the part of the accused, the paper will close with a look forward to the future of this very troubled and all but eliminated defence in Canadian criminal law.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".