Examining the predictive validity of the Ontario Domentic Assault Risk Assessment (ODARA) in police departments and pretrial service agencies in the United States
Bibliographic record
Abstract
Intimate partner violence (IPV) is a pattern of coercive and controlling behaviors that includes emotional, verbal and psychological abuse, sexual coercion and assault, and other forms of physical violence. Without intervention, IPV tends to escalate in frequency and severity over time and, in extreme cases, intimate partner violence can lead to homicide. The need to determine and treat the most serious cases of IPV has brought about a proliferation of statistical assessments and standardized decision-making tools. One such tool is the ODARA, which has performed very well in tests of predictive validity in Ontario, Canada, and may be appropriate for implementation in the U.S. criminal justice system. However, no tests of the predictive validity of the ODARA have been conducted in the U.S.The current research will provide an empirical base for implementation of the ODARA (or a modified version of the ODARA) in the U.S. criminal justice context as well as recommendations for implementation within police departments and pretrial services. The specific aims of the study are as follows: (1) To examine the predictive validity (at 1, 3, and 5 year follow-up) of the ODARA as used by police in a single county (Saco) and in 2 additional counties in the state of Maine, (2) To examine the predictive validity (at 1, 3, and 5 year follow-up) of the ODARA as used by pretrial services in 2 Counties (Denver, CO and Travis County, TX). The inclusion of multiple sites (with geographic and demographic diversity) and larger sample sizes will also assist with providing justification for generalization.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".