A Cross-National Data Collaboration of Domestic Violence Specialist Courts: A Research Note
Bibliographic record
Abstract
Specialized domestic violence (DV) courts exist in a number of common law countries. While site-specific evaluations show promise, there remains much dissimilarity to the processes and components of these courts that raise questions about cross-national comparison. This article discusses a project to reduce data variation by applying a standardized data grid in court sites in three different countries. Part of a larger research program of the Canadian Observatory on Justice Responses to Intimate Partner Violence, the pilot shows that, with site-specific adaptions, a standardized grid can reduce variation and is feasible as a data collection instrument. However, the pilot also reveals how the shared objectives of specialized DV courts across countries are undermined by the absence of core data especially that related to victims. Further, standardized cross-national court data collection can unhelpfully minimize differences in local legal and social cultures that are important to justice system priority-setting.
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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 | 0.164 | 0.181 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".