Recent Research (N = 9,305) Underscores the Importance of Using Age-Stratified Actuarial Tables in Sex Offender Risk Assessments
Bibliographic record
Abstract
A useful understanding of the relationship between age, actuarial scores, and sexual recidivism can be obtained by comparing the entries in equivalent cells from "age-stratified" actuarial tables. This article reports the compilation of the first multisample age-stratified table of sexual recidivism rates, referred to as the "multisample age-stratified table of sexual recidivism rates (MATS-1)," from recent research on Static-99 and another actuarial known as the Automated Sexual Recidivism Scale. The MATS-1 validates the "age invariance effect" that the risk of sexual recidivism declines with advancing age and shows that age-restricted tables underestimate risk for younger offenders and overestimate risk for older offenders. Based on data from more than 9,000 sex offenders, our conclusion is that evaluators should report recidivism estimates from age-stratified tables when they are assessing sexual recidivism risk, particularly when evaluating the aging sex offender.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".