Putting Trials on Trial: Sexual Assault and the Failure of the Legal Profession
Bibliographic record
Abstract
Over the past few years, public attention focused on the Jian Ghomeshi trial, the failings of Judge Greg Lenehan in the Halifax taxi driver case, and the judicial disciplinary proceedings against former Justice Robin Camp have placed the sexual assault trial process under significant scrutiny. Less than one percent of the sexual assaults that occur each year in Canada result in legal sanction for those who commit these offences. Survivors often distrust and fear the criminal justice process, and as a result, over ninety percent of sexual assaults go unreported. Unfortunately, their fears are well founded. In this thorough evaluation of the legal culture and courtroom practices prevalent in sexual assault prosecutions, Elaine Craig provides an even-handed account of the ways in which the legal profession unnecessarily - and sometimes unlawfully - contributes to the trauma and re-victimization experienced by those who testify as sexual assault complainants. Gathering conclusive evidence from interviews with experienced lawyers across Canada, reported case law, lawyer memoirs, recent trial transcripts, and defence lawyers’ public statements and commercial advertisements, Putting Trials on Trial demonstrates that - despite prominent contestations - complainants are regularly subjected to abusive, humiliating, and discriminatory treatment when they turn to the law to respond to sexual violations. In pursuit of trial practices that are less harmful to sexual assault complainants as well as survivors of sexual violence more broadly, Putting Trials on Trial makes serious, substantiated, and necessary claims about the ethical and cultural failures of the Canadian legal profession.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.061 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".