Risk assessment for domestically violent men: Tools for criminal justice, offender intervention, and victim services.
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
"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)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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