Putting Definitions to Work: Reflections from the Canadian Domestic Homicide Prevention Initiative with Vulnerable Populations
Bibliographic record
Abstract
Abstract Definitions of domestic homicide shape data collection and prevention efforts and, consequentially, our understanding of these crimes. This chapter explores issues related to defining domestic homicide in the context of our work with the Canadian Domestic Homicide Prevention Initiative with Vulnerable Populations (CDHPIVP). We discuss selected case studies to demonstrate what cases are included and excluded in this work and to highlight the importance of understanding our narrower, project-based definition in relation to the larger context of domestic violence-related homicides and deaths. By considering how victims and perpetrators are identified when defining domestic violence, we illustrate how undercounting of domestic homicide may occur, contributing to the “dark figure” of domestic homicide. Furthermore, we argue that cases from certain groups, such as Indigenous women in Canada, may be systematically excluded from definitions of domestic homicide. In reflecting on these issues and cases, our aim is to advance calls for consistency and transparency in definitions to allow for stronger research across jurisdictions (Fairbairn, Jaffe, & Dawson, 2017; Jaffe et al., 2017), as well as to support efforts of initiatives such as domestic violence death review committees (DVDRCs) in their work to prevent domestic homicides.
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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 | 0.096 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.067 | 0.054 |
| Scholarly communication | 0.030 | 0.011 |
| Open science | 0.011 | 0.015 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".