Development of Vermont Assessment of Sex Offender Risk-2 (VASOR-2) Reoffense Risk Scale
Bibliographic record
Abstract
The present study aimed to revise the Vermont Assessment of Sex Offender Risk (VASOR) Reoffense Risk Scale, a commonly used sex offender risk assessment tool. The revised tool was named the VASOR-2. Among models tested to revise the scale, a logistic regression model showed the best balance between simplicity of use, goodness of fit, and internal validity (as tested with K-10 cross-validation), and maximized predictive accuracy. Predictive accuracy was tested using four meta-analytically combined data sets drawn from Canada and Vermont (N = 1,581). At 5-year fixed follow-up, the predictive accuracy for sexual recidivism for VASOR-2 (AUC = .74) was similar to the VASOR (AUC = .71). The findings show the VASOR-2 is well calibrated with observed recidivism rates for all but the highest risk sex offenders. The instrument showed good interrater reliability (ICC = .88). An advantage of the VASOR-2 is that it has fewer items and simpler scoring instructions than the VASOR. Norms are presented for a contemporary, nonselected, routine sample of Vermont sex offenders (n = 887).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".