A Situational Approach to Incapacity and Mental Disability in Sexual Assault Law
Bibliographic record
Abstract
Prosecutions for sexual assault most often focus on whether the Crown has proven that the complainant did not consent to the sexual activity in issue, based on her subjective state of mind at the time of the offence. However, Canadian criminal law also provides that no consent is obtained where the complainant is incapable of consenting. In cases where the complainant has a mental disability affecting cognition or decisionmaking, prosecutors in Canada have been reluctant to argue that the complainant was incapable of consenting. In this article, the authors agree that claims of incapacity should be used sparingly, but contend that the doctrine of incapacity may be applicable and useful in some cases where the accused has exploited the complainant’s disability. They argue that capacity to consent to sexual activity should be defined situationally, rather than as an all-or-nothing measure. Since consent is given to a specific person in a specific circumstance, incapacity should be also assessed by reference to the particular context of the case. This approach to incapacity has been adopted in English and American cases, which provide examples of how it might be applied and understood in Canada. A situational definition of incapacity offers some legal recognition of the particular challenges faced by women with mental disabilities with respect to sexual abuse, without disqualifying them from any lawful sexual activity in other contexts.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".