Bibliographic record
Abstract
One innovative justice system response, since the beginning of the nineties, has been the development of specialised domestic violence courts in different countries. These new mechanisms for dealing with cases of intimate partner violence addressed many of the identified problems of non-specialised processes, such as improved victim services and programs for abusers, greater collaboration between family and criminal matters, and an array of new law enforcement policies and legislation, such as pro-arrest policies and domestic violence legislation. In Canada, different provincial and territorial jurisdictions have implemented specialised domestic violence courts. However, emerging research raises critical questions on the role of courts in society. Judges, defence attorneys, prosecutors and victim advocates, while expressing support for a specialised domestic violence court model, are still concerned about issues of victim safety, the safety of children, and offender recidivism. In 2007, experts from the Canadian observatory on the justice systems response to intimate partner violence, commenced the groundwork to fully understand the process and the effectiveness of specialised domestic violence courts. In this presentation, we will discuss specialised justice responses to domestic violence cases versus non specialised responses in Canada. This will lead to talk about the importance of collaboration among diverse stakeholders in order to establish proper data collection about offenders and victims of intimate partner violence entering in the justice system. It will be an opportunity to discuss the research agenda of the Canadian observatory.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".