What Sexual Recidivism Rates Are Associated With Static-99R and Static-2002R Scores?
Why this work is in the frame
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Bibliographic record
Abstract
Empirical actuarial risk tools are routinely used to assess the recidivism risk of adult sexual offenders. Compared with other forms of risk assessment, one advantage of actuarial risk tools is that they provide recidivism rate estimates. Previous research, however, suggests that there is considerable variability in the recidivism rates associated with the most commonly used sexual offender risk assessment tools (Static-99/R, Static-2002/R). The current study examined the extent to which the variability in the recidivism rates across 21 Static-99R studies (N = 8,805) corresponded to the normative groups proposed by the STATIC development group (routine, treatment, high risk/high need). We found strong evidence that routine (i.e., complete) samples were, on average, less likely to reoffend with a sexual offense than offenders in the high-risk/high-need samples (i.e., those explicitly preselected on risk-relevant variables external to STATIC scales). The differences between routine/complete and high-risk/high-need samples, however, were only consistently observed for offenders with low or moderate scores; for offenders with high STATIC scores, the 5-year sexual recidivism rates for these two groups were not meaningfully different. There was only limited evidence to support treatment samples as a distinct sample type; consequently, the use of separate normative tables for treatment samples is not recommended. The current results reinforce the value of regularly updating the norms for empirical actuarial risk tools. Options are discussed on how STATIC scores could be used to inform recidivism rates estimates in applied assessments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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