SEXUAL ASSAULT OF WOMEN WITH MENTAL DISABILITIES: RETHINKING INCAPACITY AND NON-CONSENT
Bibliographic record
Abstract
Women with mental disabilities confront sexual assault at an alarming rate. We have argued elsewhere that this reality should be reflected in our understanding of the concepts of consent and capacity in the criminal law of sexual assault (Benedet and Grant 2007a; Benedet and Grant 2007b). In this paper, we examine recent developments in Canadian sexual assault law and consider whether they are adequate to recognize the experience of sexual assault for women with mental disabilities. In using the term mental disability, we refer to any developmental disability, psychiatric condition or other chronic, non-episodic disability that affects cognition or decision-making. Of course, the range of conditions that might fall within this category is vast and its boundaries not clearly fixed. Disability is both bio-medically and socially constructed in ways that shift with time and place. We are focusing on cases where the complainant is an adult woman with impairments in cognition, memory, and/or intellectual development that affect her ability to understand and make decisions about her sexuality. Our cases include those with formal diagnoses, such as Down syndrome or autism, as well as brain injuries and impairments for which there is no identified cause or label. This variation leads to different challenges for each woman, yet despite these differences we see several issues of common concern.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.050 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.012 |
| 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".