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 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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".