Predictive Validity of the Dynamic Risk Assessment for Offender Re-Entry Among Intimate Partner Violence Offenders
Bibliographic record
Abstract
Intimate partner violence (IPV) is among the most common acts of violence against women worldwide, making it a major global threat to women's health and safety.The assessment and management of IPV offenders are therefore vital tasks in criminal justice systems.The current study examined whether the DRAOR, a case management tool, predicted repeat partner abuse among 112 male IPV offenders in Iowa, U.S.While the DRAOR did not predict IPV recidivism in this sample, it appears to be useful for informing case management decisions among partnerviolent men.Risk factors that emerged as important treatment targets were poor attachment with others, substance abuse, anger/hostility, opportunity/access to victims, problematic interpersonal relationships, and overall acute risk.Further research is needed to improve the utility of the DRAOR for predicting IPV recidivism, but this study tentatively supports the use of the DRAOR for supervising IPV offenders until an IPV-specific case management tool is developed.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".