Expert Evidence to Counteract Jury Misconceptions about Consent in Sexual Assault Cases: Failures and Lessons Learned
Bibliographic record
Abstract
This century has seen dramatic changes in the way in which sexual offences, particularly against children, are prosecuted in Australia, Canada, New Zealand, the United Kingdom and the United States of America. These jurisdictions have acknowledged the potential of myths and misconceptions about how a victim will behave, both during and after a sexual assault, to exert an undue influence on jurors. Expert evidence to educate jurors about common rape myths that apply to issues of consent has been used to redress this issue. However, such expert evidence poses significant challenges for the lawyers and experts. This article explores the effectiveness of educative expert evidence through analysis of an illustrative contemporary Australian child sexual assault case where the authors interviewed some of the jurors and other trial participants about their perceptions of the expert evidence. Practical suggestions to improve educative expert evidence are identified and explained.
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 | 0.179 | 0.352 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".