Legal Representation for Complainants of Sexual Violence in the Criminal Justice System: A Proposal to Advance Women's Equality
Bibliographic record
Abstract
Very few survivors of sexual violence choose to engage the Canadian criminal justice system despite the fact that we expect law to be an effective tool to combat sexual violence. Since the vast majority of sexual violence survivors are female, the criminal justice system is failing women. This failure is largely because of the harm it causes by re-victimizing sexual assault complainants. Much of that harm arises from misunderstandings about trauma and from the existence of rape myths and gender stereotypes.. I argue that the criminal justice system’s treatment of female sexual violence complainants violates their section 7 and 15 Charter rights, and represents gender discrimination under the Canadian Human Rights Act. I further propose that one key remedy to remediate this harmful and discriminatory treatment is to provide state-funded trauma-informed legal representation for sexual violence complainants throughout their engagement with the criminal justice system.\nI present my thesis in an integrated article format using three articles, and apply a doctrinal analysis to primary and secondary sources of law, jurisprudence, articles, and research. The analysis also draws on my many years of experience as a former Crown prosecutor, and as a lawyer currently in private practice, working directly with sexual assault victim-survivors. \nThe first article lays out my proposed model for legal representation for sexual violence survivors and reviews other international systems of victim representation. In the second, I argue the merits of a Charter challenge on behalf of sexual violence complainants, relying on Supreme Court jurisprudence to demonstrate that current practices and omissions in government policy result in adverse impact and systemic discrimination against sexual violence complainants. The third article provides an analysis of the policies that underpin the treatment of female sexual violence complainants, this time using the lens of the Canadian Human Rights Act, demonstrating that these inadequacies perpetuate systemic discrimination against women. All three articles conclude that an effective remedy for this discriminatory treatment is the provision of state-funded trauma-informed legal counsel to complainants of sexual violence to protect their rights and to better facilitate equal access to justice.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.018 | 0.035 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.011 | 0.007 |
| 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".