An indepth actuarial assessment for wife assault recidivism: The Domestic violence risk appraisal guide.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 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 | 0.002 | 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 it