A Longitudinal Study of Dynamic Risk, Protective Factors, and Criminal Recidivism: Change Over Time and the Impact of Assessment Timing
Bibliographic record
Abstract
Risk assessment and risk management are central to most decisions made about offenders, particularly when considering community release after a period of incarceration. Although the field of risk assessment has progressed considerably, there remain limitations within current practices. The present research uses a variety of sophisticated statistical techniques to examine the systematic assessment and reassessments of risk in a large sample (N = 3498) of New Zealand parolees. The validity of the Dynamic Risk Assessment for Offender Re-entry (DRAOR), a measure of dynamic risk and protective factors, was assessed across time in all offenders released on parole. The measure demonstrated acceptable psychometric properties, although future research should seek to refine the subscales as suggested by its factor structure. Beyond validating the DRAOR, this study showed that reconvictions and criminal reconvictions during a two-year follow-up period can be accurately predicted from dynamic risk factors and protective factors (as measured by the DRAOR). Stable and acute dynamic risk scores decreased over time while protective factor scores increased, suggesting that the DRAOR is sensitive to change. Recidivists differed from non-recidivists in stable dynamic risk and protective factors in the month prior to follow-up end and in acute dynamic risk in the second month prior to follow-up end. Reconvictions were accurately predicted from monthly average Stable Risk beginning at parole start and continuing for 12 months of assessments, while Protective Factors were predictive for the first 4 months only. These results indicate that the DRAOR has promise as a valid tool for risk assessment and risk management. The findings of this study highlight the mechanisms by which risk changes over time and provides support for a transitional model of offender re-entry focusing on dynamic risk and protective factors.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".