Weapon Use Increases the Severity of Domestic Violence but Neither Weapon Use nor Firearm Access Increases the Risk or Severity of Recidivism
Bibliographic record
Abstract
Use of weapons is a risk factor for domestic violence severity, especially lethality. It is not clear, however, whether access to firearms itself increases assault severity, or whether it is characteristic of a subgroup of offenders who are more likely to commit severe and repeated domestic assault. This reanalysis of 1,421 police reports of domestic violence by men found that 6% used a weapon during the assault and 8% had access to firearms. We expected that firearm use would be rare compared to other weapons and that actual weapon use rather than firearm access would increase the severity of domestic assaults. Firearm access was associated with assault severity, but this was mostly attributable to use of nonfirearm weapons. Weapon use was associated with older age, lower education, and relationship history as well as to assault severity. Victims were most concerned about future assaults following threats and actual injuries. Although firearm access and weapon use were related to actuarial risk of domestic violence recidivism, neither predicted the occurrence or severity of recidivism. We conclude that, consistent with previous research in the United States and Canada, firearm use in domestic violence is uncommon even among offenders with known firearm access. Weapon use is characteristic of a subgroup of offenders who commit more severe domestic violence, and seizure of weapons may be an effective intervention.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".