Bibliographic record
Abstract
The Violence Risk Scale (VRS) uses ratings of static and dynamic risk predictors to assess violence risk, identify targets for treatment, and assess changes in risk following treatment. The VRS was rated pre- and posttreatment on a sample of 150 males, mostly high-risk violent offenders many with psychopathic personality traits. These individuals attended a high-intensity institution-based cognitive-behavioral-oriented violence reduction treatment program in Canada and were then followed up for approximately 5 years postrelease to determine court adjudicated community violent recidivism. VRS scores significantly predicted violent recidivism. Measurements of risk reduction using dynamic VRS predictors were significantly correlated with reduction of violent recidivism after controlling for various potential confounds. The results suggest that, in a high-risk group of offenders with significant psychopathic traits, the VRS demonstrated predictive validity and the dynamic predictors can be used to assess treatment progress, which is linked to a specific criterion variable, thus, fulfilling the criteria for causal dynamic predictors set forth by Kraemer et al.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".