What does it mean when age is related to recidivism among sex offenders?
Bibliographic record
Abstract
Age is a robust predictor of recidivism and an item on actuarial tools commonly used to predict sexual violent recidivism among sex offenders. However, little is known about whether or how much offenders' risk diminishes as a result of aging. In the first of two studies, we examined the sexual and violent recidivism of 533 sex offenders who were over age 50 on release. Age at index offense was at least as good at predicting both outcomes as was age at release, and age at index offense provided at least as much incremental validity in the prediction of violent recidivism to scores on a brief static actuarial tool. Neither age added incrementally to static score in the prediction of sexual recidivism. The second study examined how well age at first offense, age at index offense, and age at release predicted violent recidivism among 527 sex offenders aged 13 to 79 at release. Age at first offense predicted best. When age was removed from score on the Sex Offender Risk Appraisal Guide, all ages added incrementally but age at release least to SORAG score. When participants were divided into quartiles based on age at index offense, there was no evidence from any quartile that age at release predicted violent recidivism better than age at first offense. The authors concluded that age at release is a poor index of within-subject changes in risk of sexual or violent recidivism. No adjustment to a sex offender's score on a comprehensive actuarial tool that includes age at first or index offense should be made simply because the offender is older.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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; both teacher heads agree on what is shown here.
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".