Predicting Recidivism in Sex Offenders Using the SVR-20: The Contribution of Age-at-release
Bibliographic record
Abstract
Sex offenders (N = 468) were released from custody and recidivism outcome was recorded. The Sexual Violence Risk-20 (SVR-20) was scored for each offender and the relationship between age-at-release and SVR-20 item and total scores was examined. SVR-20 total scores were not correlated with age-at-release (r = .-057). SVR-20 scores were combined with a score representing the age of the offender at their release from custody. On the basis of ROC analysis, predictive accuracy was significantly enhanced when age-atrelease was included in the risk score. We suggest that the SVR-20, and perhaps other similar risk instruments, could be improved by including age-at-release information. We discuss the possibility that the advantage obtained by empirical actuarial instruments may be due in part to their close relation with age-at-release.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".