Taking the Stand: Access to Justice for Witnesses with Mental Disabilities in Sexual Assault Cases
Bibliographic record
Abstract
In this article the authors argue that the existing adversarial trial process often prevents the stories of sexual assault complainants with mental disabilities from being heard in court. Relying on social science evidence, the authors argue that subjecting a woman with a mental disability to a rigorous cross-examination with repeated and leading questions, in a manner that is confrontational and often accusatory, is probably the worst way to get her story heard accurately in court. It is likely to unfairly undermine her credibility and to result in unjustified acquittals or in prosecutors deciding not to pursue a case. The article examines the challenges posed by traditional methods of cross-examination for witnesses with cognitive, developmental, or intellectual disabilities that affect their ability to recall, process, and communicate information, suggesting that existing Criminal Code accommodations are inadequate to address these concerns. Cross-examination should be conducted in a way that respects both the right of the accused to a fair trial and the complainant’s right to sex equality. Relying on developments in other jurisdictions, the authors recommend adopting a system of victim support persons or intermediaries, which would allow witnesses with mental disabilities to have assistance in understanding questions and in communicating their evidence to the court as fully as possible. Judges should also be given explicit legislative authority to intervene to disallow questions that are inappropriate based on the particular witness’s abilities. Such accommodations facilitate rather than impede the truth-seeking function of a trial and are not inconsistent with the fair trial rights of the accused.
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".