Dynamic sexual offense risk assessment using the VRS-SO with indeterminate sentenced men
Bibliographic record
Abstract
Indeterminate detention (ID) is a high stakes sanction reserved for exceptionally high risk-high need (HRHN) persons who are deemed to pose an undue risk to public safety. It is one of the most extreme measures that is routinely taken by justice systems to manage sexual violence risk and prevent sexual and violent recidivism. Naturally, risk assessment is most frequently employed as a mechanism to keep dangerous people in custody; but seldom is risk assessment viewed as a possible ticket out for men with an ID designation who have made substantive risk changes and whose risk can be safely managed in the community. This article features applications of a dynamic sexual violence risk assessment and treatment planning tool, the Violence Risk Scale-Sexual Offense version (VRS-SO), with ID individuals and other HRHN men, to assess risk in a dynamic manner to inform risk management efforts and release decisions. VRS-SO data on an ID sample are presented along with clinical illustrations of dynamic risk assessment. Several propositions are made with supporting data from VRS-SO normative research with treated sexual offending samples regarding the use of dynamic tools with ID men and the perils and pitfalls of relying solely on static measures. Ultimately, dynamic risk instruments can be used to track progress and monitor risk change over multiple assessments to inform release and reintegration decisions with ID persons. In this regard, dynamic assessment has the potential to help, rather than hinder, reintegration of ID sentenced persons and can inform safe, fair, and humane decisions.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".