Bibliographic record
Abstract
The Supreme Court of Canada requires accused persons to prove automatism on a balance of probabilities. The Court justified reversing the burden of proof by appealing to the impossibility of requiring the Crown to disprove automatism claims and the need to ensure accused persons do not feign a defence. In so doing, however, the Court failed to consider the economic impact of its decision. As pleading automatism requires calling an expert witness, it is likely that many impecunious accused are unable to call required testimony strictly based on their financial status. In other instances, the cost of conviction and punishment might be less than hiring an expert and thus deter an accused from pleading automatism. To address these problems, I develop an alternative approach for proving automatism that requires both Crown and defence share the burden of proof. Adopting this approach is necessary, I maintain, to justify the infringement of the presumption of innocence inherent in requiring accused persons to prove an automatism defence.
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 | 0.024 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.022 | 0.016 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".