Validating the Dynamic Risk Assessment for Offender Re-Entry (DRAOR) in a Sample of U.S. Probationers and Parolees
Bibliographic record
Abstract
The foundation for effective case management is rooted in the use of a validated risk assessment.The present study sought to validate the Dynamic Risk Assessment for Offender Re-entry (DRAOR) among a sample of probationers and parolees (n = 391) in the state of Iowa.Scores across the DRAOR domains were able to differentiate between recidivists and non-recidivists when examining technical violations and any recidivism, although were unable to differentiate between those offenders who were re-arrested and those who remained crime free.An examination of the psychometric properties of the scale suggested that the DRAOR is a valid risk assessment tool.Additionally, Stable dynamic risk factors represented the strongest predictor of technical violations, although were unable to predict rearrest.The predictive utility of the various domains (i.e.Stable, Acute, and Protective) suggested that case managers would benefit from utilizing the DRAOR in the everyday supervision of offenders.U.S. DRAOR
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".