Bibliographic record
Abstract
Courts have confined the common-law defence of sane automatism by defining disease of the mind, a requisite component of insane automatism, so broadly as to ensnare anyone whose automatism might recur and lead to violence. This definition of insane automatism in terms of dangerousness means that persons found innocent of wrongdoing are detained and possibly confined for their own good (as others see it) or for what they might do in the future, in the absence of the only justification for giving force to these reasons consistent with respect for their autonomy. That justification is that the person acquitted is suffering from a mental disorder that severely impairs his capacity for autonomous action, justifying diminished respect. The Criminal Code definition of legal insanity honours this justification, but the common-law definition of insane automatism does not. Accordingly, a disease of the mind should be redefined as any mental disorder that renders the person generally incapable of appreciating the reasonably foreseeable consequences of his actions or of understanding information relevant to executing his conception of wellbeing. While this definition would channel fewer acquittees into the post-trial disposition hearing, it provides as much protection from dangerous persons as a free society permits. If impaired autonomy rather than dangerousness were the criterion of insane automatism, there would be no need to make the pleas of sane and insane automatism mutually exclusive or to place the burden of proving involuntariness on the accused.
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 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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.031 | 0.023 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".