Putting Trials on Trial: Sexual Assault and the Failure of the Legal Profession
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it