Examining Trajectories of Change on the Dynamic Risk Assessment for Offender Re-Entry (DRAOR)
Bibliographic record
Abstract
Dynamic risk scales have largely been evaluated using singular assessment scores, including those obtained at the start of supervision. While this approach includes assessment of dynamic factors, it ignores changes with reassessment, failing to examine whether an instrument is truly dynamic in nature. This is problematic, as proximal risk assessments have consistently outperformed baseline assessments in the prediction of recidivism. In the current study, we examined the dynamic properties of the Dynamic Risk Assessment for Offender Reentry (DRAOR) in 4,736 adults on community supervision in Iowa, United States ( N = 33,965 assessments). As expected, while clients demonstrated statistically significant changes on the DRAOR domains over time, changes were small in magnitude. We also examined the predictive validity of baseline and proximal DRAOR total and domain scores on criminal recidivism and revocation in a larger sample of 11,421 adults in the same jurisdiction. While DRAOR baseline scores did predict both outcomes, prediction did not improve with proximal scores. This conflicted with expected findings from previous research on the DRAOR in New Zealand. The results of both of these research questions indicate there was an overall lack of change reflected in this sample. Potential issues regarding implementation fidelity are discussed. Additional research is needed to examine the dynamic properties of the DRAOR in Iowa given the importance of reassessment data in community corrections.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".