A Critical Exploration of the Use of Mental Health Records in Rape Trials \n
Bibliographic record
Abstract
Commentators discussing the cross-examination of rape complainants have tended to focus on sexual history evidence and character evidence more generally. The defence use of psychiatric evidence has, in contrast, received very little attention to date. This thesis examines the use of women’s mental health records in rape trials, arguing that such use is a further demonstration of the resilient focus on the complainant’s character and behaviour in rape trials. Against a backdrop of wide stigmatisation and victimisation of those with mental health problems, this thesis aims to analyse the existing literature on use of mental health records in rape trials, while also serving to highlight the need for more sustained critical research and reflection on the treatment of women with mental health problems within the criminal justice system. The thesis argues that the law governing the use of mental health records in rape trials is significantly flawed and requires reform, taking inspiration from the law in two other jurisdictions – Canada and New South Wales.
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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 | 0.262 | 0.460 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.027 | 0.043 |
| Scholarly communication | 0.026 | 0.018 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".