Risk assessment for domestically violent men: Tools for criminal justice, offender intervention, and victim services.
Bibliographic record
Abstract
"From a domestic violence victim's first contact with authorities through the offender's bail, sentencing, parole, and treatment program, criminal justice officers and clinicians must make informed decisions about which cases need the most attention and must ensure targeted provisions are in place to prevent recurrences of violence. Hilton, Harris, and Rice make a powerful case for using actuarial risk assessments to predict recidivism in male domestic violence offenders. These assessments, the Ontario Domestic Assault Risk Assessment (ODARA) and the Domestic Violence Risk Appraisal Guide (DVRAG), are the first in the field. The authors assert that making it public policy to use these tools systematically will reduce the number of violent assaults on women by their partners. The book draws on the authors' in-depth empirical studies of violent men and their extensive experience with recidivism risk assessment in policing, court cases, offender assessment, and victim services. It also functions as a user's manual�replete with all the scoring, reporting, and interpreting details needed to effectively use the ODARA/DVRAG system. The inclusion of case examples, FAQs, scoring tools and forms, and sample assessment reports makes this an excellent resource for any professional working directly with domestic violence offenders or training criminal justice officers to conduct risk assessments"--Jacket. (PsycINFO Database Record (c) 2010 APA, all rights reserved)
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".