Investigating the Dynamic Risk Assessment for Offender Re-entry’s (DRAOR) Ability to Operate as a Weighted Risk Prediction Instrument
Bibliographic record
Abstract
Dynamic risk and protective factors refer to a collection of psychosocial variables that have been empirically linked to an increased or decreased likelihood of engaging in future criminal behaviour.Monitoring such factors is, therefore, a vital task in the post-incarceration community reintegration process.The current study examined whether weighting could augment the discrimination of the Dynamic Risk Assessment for Offender Re-entry (DRAOR; Serin, 2007Serin, , 2015Serin, , 2017)), a promising case management instrument composed of dynamic risk and protective factors, in two samples of general justice involved individuals drawn from New Zealand (n = 3,648) and Iowa (n = 510).Two weighting approaches were investigated across subscales, outcomes, assessment periods, and samples.Although weighting did not significantly improve discrimination in either sample, the present research provides further support of the DRAOR's utility as a risk prediction and case management tool.
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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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".