Exploratory comparison between fatal and non-fatal cases of intimate partner violence
Bibliographic record
Abstract
Abstract Purpose Much has been written about intimate partner homicide (IPH), but empirical examinations have been less rigorous and mostly descriptive in nature. The purpose of this paper is to provide an exploration of the characteristics of fatal intimate partner violence (IPV) cases. Design/methodology/approach A direct comparison of fatal IPHs with both a matched sample of non-fatal IPV cases and a random selection of non-fatal IPV cases is made on a number of offence, offender, victim characteristics and risk-relevant variables. Findings Despite assertions that domestic homicide is different than domestic violence, in general, few notable differences emerged among the groups. Prior domestic incidents differed between the matched fatal and non-fatal cases, where a greater proportion of the homicide perpetrators had a prior domestic incident. Other differences that were found revealed that more non-fatal perpetrators had substance abuse problems, younger victims and been unemployed at the time of the offence. However, differences were minimal when fatal and non-fatal IPV perpetrators were matched on demographic features and criminal history. Originality/value This study highlights that there may be few features that distinguish IPH and non-fatal violence. Rather than be distracted with searching for risk factors predictive of fatality, we should evaluate IPV risk using broad-based approaches to determine risk for reoffending and overall severity of reoffending.
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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 | 0.004 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".