The Impact of Known Criminals on the Proportion and Seriousness of Intimate Partner Violence Incidents
Bibliographic record
Abstract
This study examines a hypothesis that has not received adequate scrutiny: that an important proportion of intimate partner violence (IPV) incidents, particularly those that are more serious, involve generalist offenders known to the police. Many criminological theories and empirical studies suggest that offenders are often generalists, yet few IPV studies consider this hypothesis. Based on a sample of 52,149 IPV incidents recorded by police, we found that 31% of IPV incidents involved suspects only with criminal records for non-IPV criminality, 9% involved victims only with criminal records for non-IPV criminality, and 14% involved both suspects and victims with criminal records for non-IPV criminality. Thus, 45% of IPV offenders and 23% of IPV victims had criminal records for non-IPV criminality. Multilevel regression analyses reveal that controlling for prior IPV incidents, community context, and other individual and couple variables, IPV offenders with criminal records are 16% more likely to be involved in more serious incidents, and victims of IPV with criminal records are 17% more likely to be involved in more serious incidents. In addition, IPV incidents for which both suspects and victims had criminal records were 46% more likely to be more serious incidents. These results suggest that generalist criminals known by police have an important impact on the proportion of IPV incidents, particularly the more serious ones.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".