Some notes on the validation of VRS-SO static scores
Bibliographic record
Abstract
The present study was a psychometric examination of Violence Risk Scale-Sexual Offender version (VRS-SO; Wong, S., Olver, M. E., Nicholaichuk, T. P., & Gordon, A. (2003 Wong, S., Olver, M. E., Nicholaichuk, T. P., & Gordon, A. (2003). The violence risk scale: Sexual offender version (VRS-SO). Saskatoon: Regional Psychiatric Centre and University of Saskatchewan. [Google Scholar]). The violence risk scale: Sexual offender version (VRS-SO). Saskatoon: Regional Psychiatric Centre and University of Saskatchewan) static item scores in a Canadian multisite sample of 668 treated adult male sexual offenders. Exploratory factor analysis (EFA) of 13 nonredundant Static-99R and VRS-SO static items generated three factors labelled Youthful Aggression, Sexual Criminality, and General Criminality. The factor and total scores converged with Static-99R and VRS-SO dynamic factor scores. Scores on the VRS-SO static items, EFA-derived factors, and total score each significantly predicted 5- and 10-year sexual, violent, and general recidivism through ROC analyses. Cox regression survival analyses showed all three factors uniquely predicted sexual recidivism to varying degrees in the overall sample; however, only Youthful Aggression and General Criminality uniquely significantly predicted violent and general recidivism in the overall sample and among sexual offender subgroups. Implications for theory, clinical practice, and instrument refinement are discussed.
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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 | 0.280 | 0.385 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".