Dynamic Risk Scales Degrade Over Time: Evidence for Reassessments
Bibliographic record
Abstract
Risk tools containing dynamic (potentially changeable) factors are routinely used to evaluate the recidivism risk of justice-involved individuals. Although frequent reassessments are recommended, there is little research on how the predictive accuracy of dynamic risk assessments changes over time. This study examined the extent to which predictive accuracy decreases over time for the ACUTE-2007 and the STABLE-2007 sexual recidivism risk tools. We used two independent samples of men on community supervision ( N Study 1 = 795; N Study 2 = 4,221). For all outcomes (sexual, violent, and any recidivism [including technical violations]), reassessments improved predictive accuracy, with the largest effects found for the most recent assessment (i.e., those closest in time prior to the recidivism event). Based on these results, we recommend that ACUTE-2007 assessments occur at least every 30 days and that the STABLE-2007 assessments occur every 6 months or after significant life changes (e.g., successful completion of treatment).
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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 | 0.169 | 0.519 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".