Barriers to safety for victims of domestic homicide
Bibliographic record
Abstract
Research on domestic homicide has focused on risk factors presented by perpetrators such as prior violence, threats to kill, stalking, access to weapons, mental health concerns, controlling behaviour and separation. However, there has been less focus on the barriers that victims face regarding finding support, increasing personal safety and decreasing violence and risk of homicide. The present study explored 20 potential barriers that female domestic homicide victims faced using 183 cases occurring between 2002 and 2012 from the Ontario (Canada) Domestic Violence Death Review Committee to examine the presence and frequency of these barriers within the sample. Using two-step cluster analysis, different profiles of barriers were identified that centred on victims’ fear, social isolation and mental health. The study is limited in being a post hoc analysis of homicides and no causal links can be made. The implications of this finding are discussed in the context of risk assessment, risk management and safety planning.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".