Establishing Canada's First Integrated Domestic Violence Court: Exploring Process, Outcomes, and Lessons Learned
Bibliographic record
Abstract
The establishment of domestic violence courts has resulted in significant improvements in responses to family violence, but these courts have generally dealt only with criminal cases and do not address the risks that the victim and children may face in family proceedings. In some locations in the USA, courts have been established to deal with both criminal and family proceedings that arise from a domestic violence situation. This paper describes and analyzes the establishment of the first court in Canada that hears both criminal and family cases concerning families where there are domestic violence issues. The authors report on a study of the views and experiences of 21 stakeholders (judges, Crown, criminal and family lawyers, community supports, victims, and offenders) involved in the Integrated Domestic Violence Court in Toronto. The participants generally report that the Court provides a better approach to dealing with domestic violence post separation, though there are some concerns expressed about its operations, especially by lawyers representing alleged abusers. The Integrated Domestic Violence Court is a promising example of how systems can collaborate to better protect victims and advance the interests of children.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".