Non-Specialization of Criminal Careers Among Intimate Partner Violence Offenders
Bibliographic record
Abstract
Many men arrested for intimate partner violence (IPV) commit other types of criminal offenses as well. We examined 93 IPV offenders’ general offending and tested the ability of criminal career trajectory and an IPV-specific risk assessment (Ontario Domestic Assault Risk Assessment [ODARA]) to predict post-index recidivism 7.5 years later. Most (71%) had pre-index criminal charges, and most (62%) had post-index criminal recidivism, although fewer (24%) committed post-index IPV. Pre-index criminal career (defined as none, non-violent, violent, IPV) did not predict post-index IPV, whereas the ODARA predicted post-index IPV, area under the curve (AUC = .67), as well as other offenses with a moderate or large effect size, including stalking (AUC = .78), sexual assault (AUC = .67), and non-violent offenses (AUC = .74). In line with prior research findings, we conclude that many men arrested for IPV do not specialize in their criminal careers and that risk assessment in these cases could include risk of both IPV and other offenses. Furthermore, the ODARA holds promise for assessing general risk of recidivism among IPV offenders.
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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 | 0.001 |
| 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".