An indepth actuarial assessment for wife assault recidivism: The Domestic violence risk appraisal guide.
Bibliographic record
Abstract
An actuarial tool, the Ontario Domestic Assault Risk Assessment (ODARA), predicts recidivism using only variables readily obtained by frontline police officers. Correctional settings permit more comprehensive assessments. In a subset of ODARA construction and cross-validation cases, 303 men with a police record for wife assault and a correctional system file, the VRAG, SARA, Danger Assessment, and DVSI also predicted recidivism, but the Hare Psychopathy Checklist (PCL-R) best improved prediction of recidivism, occurrence, frequency, severity, injury, and charges. In 346 new cases, ODARA and PCL-R independently predicted recidivism. An algorithm was derived for a combined instrument, the Domestic Violence Risk Appraisal Guide (DVRAG), and an experience table is presented (N=649). Results indicated the importance of antisociality in wife assault.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".