Risk Factors for Domestic Homicide: Immigrant & Canadian-born Populations
Bibliographic record
Abstract
Domestic violence is a critical human rights issue that can escalate to cases of domestic homicide. Globally, approximately 30% of women in relationships have reported experiencing violence at the hands of an intimate partner. In Canada this pattern is echoed, as over 25% of police-reported violent offences were from victims of domestic abuse. Recent research has revealed that immigrant & refugee victims experience unique risk factors that may render them more vulnerable to this form of violence. Yet, despite this burgeoning research area, and Canada’s diverse population of 6 million immigrants, there is a dearth of research pertaining to domestic violence risk factors facing immigrant victims in a Canadian context. Indeed, the shifting sociodemographic profile of Canada's population calls for culturally-informed risk assessment, risk management & safety planning tools to protect as many people as possible from domestic violence & homicide. Therefore, this study investigated factors that pertain to a victim’s vulnerability to violence across immigrant and Canadian-born populations. Although several factors, such as actual or pending separation, were shared across both demographics, other factors, such as social isolation, featured more prominently in cases of immigrant domestic homicide victims. By identifying these shared and unique characteristics, front line workers & policy makers will be informed of important trends that can influence the creation of research-based & culturally-relevant risk assessment, risk management and safety planning strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".