Actuarial risk assessment in sexually motivated intimate-partner violence.
Bibliographic record
Abstract
The present study is the first independent cross-validation of the Ontario Domestic Assault Risk Assessment (ODARA) and the Domestic Violence Risk Appraisal Guide (DVRAG) using an incarcerated high-risk sample (N = 66) of offenders released from the Austrian Prison System who have committed at least one sexually motivated offense against their actual or former intimate partners. The mean follow-up period was approximately 55 months. Both instruments showed evidence for their reliability and predictive accuracy, supporting the cross-cultural transferability of these risk assessment instruments. For the prediction of domestic violence recidivism, ODARA and DVRAG yield good predictive accuracy (area under the receiver operating characteristic curve, AUC = .71), and for general criminal and general violent recidivism, both instruments exhibit moderate effect sizes (AUC = .66-.71). Also, the results provide evidence for the discriminant validity of the ODARA. When examining the association between individual ODARA items and recidivism, only a few items were found to be related to domestic violence recidivism. The integration of the Psychopathy Checklist-Revised (PCL-R) does not add any incremental predictive accuracy to the ODARA, suggesting that ODARA items capture antisocial and psychopathic traits sufficiently even in incarcerated high-risk offenders.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".