Opening the Door Beyond the Legal System: Understanding the Use of Alternative Justice Approaches to Sexual Violence Prevention and Healing in Canada
Bibliographic record
Abstract
Victimization surveys suggest that one in three women in Canada and one in six men will experience some kind of sexual violence in their lifetimes. Rather than turn to the police, most victims turn to family or friends or shoulder the burden in silence. For every 1000 sexual assaults in Canada, only 33 are reported, and only 3 result in convictions of the perpetrator. The reality is the current criminal justice responses to sexual violence are not serving victims. When presented with alternatives to the traditional justice system, victims routinely choose them and experience much better outcomes with alternative justice approaches. In 2021, with support from the Canadian Women’s Foundation, Shift conducted research to better understand alternative justice approaches to sexual violence healing and prevention in Canada. By alternative justice approaches, we mean those activities and interventions that are outside the criminal legal system, that are victim and survivor-centred, trauma-informed, and promote prevention, accountability, justice, healing, and repair. The research project involved reviewing academic and grey literature, conducting an environmental scan, and interviewing advocates and practitioners who engage in this work. Through these three data collection methodologies, principles, practices, training, and activities have been identified, along with a series of recommendations to continue to grow and support this area of practice.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.039 | 0.020 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".