Bibliographic record
Abstract
The treatment of sexual assault complainants by defence counsel has been the site of significant debate for legal ethicists. Even those with the strongest commitment to the ethics of zealous advocacy struggle with how to approach the cross-examination of sexual assault complainants. One of the most contentious issues in this debate pertains to the use of bias, stereotype and discriminatory tactics to advance one’s client’s position. This paper focuses on the professional responsibilities defence lawyers bear in sexual assault cases. Its central claim is as follows: Defence counsel are ethically obligated to restrict their carriage of a sexual assault case (including the evidence they seek to admit, the lines of examination and cross-examination they pursue and the closing arguments they submit) to conduct that supports finding of facts within the bounds of law. Put another way, defence counsel are ethically precluded from using strategies and advancing arguments that rely for their probative value on three social assumptions about sexual violence that have been legally rejected as baseless and irrelevant.
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 | 0.036 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.029 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.020 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".