The Predictive Validity of the Two-Tiered Violence Risk Estimates Scale (TTV) in a Long Term Follow-Up of High Risk Federal Offenders
Bibliographic record
Abstract
The reduction of general and violent recidivism has long been an issue of concern within the criminal justice system in Canada.Over the past few decades, several structured risk appraisal measures have been created in order to respond to this need.The Two-Tiered Violence Risk Estimates Scale (TTV; Mills & Kroner, 2005) is a measure designed to both predict the risk of violent recidivism for an individual offender and to identify critical risk management areas.The current study examined the predictive validity of the TTV in a sample of high-risk Canadian federal offenders (n = 120).Scores on the TTV were compared to those of the Violence Risk Appraisal Guide (VRAG; Harris, Rice, & Quinsey, 1993), the Statistical Information on Recidivism Scale -Revised (SIR-R1; Nuffield, 1982), and the Psychopathy Checklist -Revised (PCL-R; Hare, 2003).Approximately 53% of the sample reoffended violently, with an overall recidivism rate of 73%.While the VRAG was the strongest predictor of violent recidivism in the sample, the Actuarial Risk Estimates (ARE) scale of the TTV produced a small, significant effect.The Risk Management Indicators (RMI) produced non-significant AUC values for all recidivism outcomes.Measure comparisons using AUC values and Cox regression showed that there were no differences in predictive validity.The results of this research are discussed in the context of the validation and reliability of the TTV, and contribute to the overall risk assessment literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".