A review of pro-arrest, pro-charge, and pro-prosecution policies as a response to domestic violence
Bibliographic record
Abstract
Summary The focus of this scoping review was to understand the overall state of research activity related to pro-arrest, pro-charge, and pro-prosecution policies in Canada. The review identified 295 articles, academic (47.5%) and gray literature (52.5%) published between 1983 and 2018 that reported on these policies as a response to domestic violence in Canada. Findings The findings acknowledged growing concerns over the ineffectiveness of these policies as a response to domestic violence. In fact, over half of the articles (57.6%) either failed to support these policies or recommended significant revisions; only a small number of articles (1.0%) favored these policies in their current form. Themes related to the effectiveness of these policies included criminalization and public awareness, survivor satisfaction, standardized police response, removal of burden from survivor to charge, and better than nothing. Themes related to the ineffectiveness included disconnect between policy and practice, revictimizes survivors, one-size fits all approach, have not adequately reduced domestic violence in Canada, lack of understanding training and education for all, failure to address structural and systemic factors, lack of trust in the criminal justice system, and success has been difficult to measure. Applications The deconstruction of these policies in this review points to the need for future research to address identified gaps in the literature and to explore alternatives that serve their intended emancipatory effect.
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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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".