Communicating the results of criterion referenced prediction measures: Risk categories for the Static-99R and Static-2002R sexual offender risk assessment tools.
Bibliographic record
Abstract
This article describes principles for developing risk category labels for criterion referenced prediction measures, and demonstrates their utility by creating new risk categories for the Static-99R and Static-2002R sexual offender risk assessment tools. Currently, risk assessments in corrections and forensic mental health are typically summarized in 1 of 3 words: low, moderate, or high. Although these risk labels have strong influence on decision makers, they are interpreted differently across settings, even among trained professionals. The current article provides a framework for standardizing risk communication by matching (a) the information contained in risk tools to (b) a broadly applicable classification of "riskiness" that is independent of any particular offender risk scale. We found that the new, common STATIC risk categories not only increase concordance of risk classification (from 51% to 72%)-they also allow evaluators to make the same inferences for offenders in the same category regardless of which instrument was used to assign category membership. More generally, we argue that the risk categories should be linked to the decisions at hand, and that risk communication can be improved by grounding these risk categories in evidence-based definitions. (PsycINFO Database Record
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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 | 0.049 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".