Improving the Predictive Accuracy of Static-99 and Static-2002 With Older Sex Offenders
Why this work is in the frame
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Bibliographic record
Abstract
Actuarial risk assessment scales and their associated recidivism estimates are generally developed on samples of offenders whose average age is well below 50 years. Criminal behavior of all types declines with age; consequently, actuarial scales tend to overestimate recidivism for older offenders. The current study aimed to develop a revised scoring system for two risk assessment tools (Static-99 and Static-2002) that would more accurately describe older offenders' risk of recidivism. Using data from 8,390 sex offenders derived from 24 separate samples, age was found to add incremental predictive validity to both Static-99 and Static-2002. After creating new age weights, the resulting instruments (Static-99R and Static-2002R) had only slightly higher relative predictive accuracy. The absolute recidivism estimates, however, provided a substantially better fit for older offenders than the recidivism estimates from the original scales. We encourage evaluators to adopt the revised scales with the new age weights.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it