Arrest History and Intimate Partner Violence Perpetration in a Sample of Men and Women Arrested for Domestic Violence
Bibliographic record
Abstract
Intimate partner violence (IPV) is a serious and prevalent problem throughout the United States. Currently, individuals arrested for domestic violence are often court mandated to batterer intervention programs (BIPs). However, little is known about the arrest histories of these individuals, especially women. The current study examined the arrest histories of men (n = 303) and women (n = 82) arrested for domestic violence and court-referred to BIPs. Results demonstrated that over 30% of the entire sample had been previously arrested for a non-violent offense, and over 25% of the participants had been previously arrested for a violent offense other than domestic violence. Moreover, men were arrested significantly more frequently for violence-related and non-violent offenses than their female counterparts. In addition, men were more likely than women to have consumed binge-levels of alcohol prior to the offense that led to their most recent arrest and court-referral to a BIP. Lastly, arrest history was positively associated with physical and psychological aggression perpetration against an intimate partner for men only, such that more previous arrests were associated with more frequent aggression. These results provide evidence that many men and women arrested for domestic violence have engaged in a number of diverse criminal acts during their lifetimes, suggesting that BIPs may need to address general criminal behavior.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".