Risk Constructs Behind Ontario Domestic Assault Risk Assessment
Bibliographic record
Abstract
Actuarial risk assessment measures are often criticized because items are typically historical and do not capture potential change. Latent variable models are used to link historical risk factors to risk domains that may be the target of intervention. Using exploratory factor analysis, we explored the latent factors of the Ontario Domestic Assault Risk Assessment (ODARA) and the extent to which factors predict general, any violent, and IPV recidivism by conducting area under the receiver operating characteristic curve (AUC). We found that the ODARA contains three factors, which could be best attributed as antisocial patterns, victim vulnerabilities, and index offense-related. Antisocial Patterns significantly predicted all outcomes, whereas Victim Vulnerabilities only predicted general reoffending, and Index Offense did not reliably predict any of the recidivism outcomes. Moreover, Antisocial Patterns predicted all recidivism outcomes as well as the ODARA total. Additionally, Antisocial Patterns was able to predict any violent and general reoffending significantly better than Victim Vulnerabilities and Index Offense. Given that only Antisocial Patterns could predict IPV recidivism, our current understanding of factors unique to IPV needs further exploration to increase understanding and conceptualization of factors most strongly associated with IPV offenses, thereby improving the assessment of risk.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".