Developing Non-Arbitrary Metrics for Risk Communication: Percentile Ranks for the Static-99/R and Static-2002/R Sexual Offender Risk Tools
Bibliographic record
Abstract
The aim of this article was to advance risk communication by examining percentile ranks as a non-arbitrary metric for quantifying risk. Although percentile ranks have a simple meaning, their calculation is complicated by ties (i.e., more than one offender having the same score). The strengths and weaknesses of percentile ranks are discussed, as are the options for calculating and presenting them in applied risk communication. As a demonstration, percentile ranks for Canadian sexual offenders were computed for the most popular sexual offender risk assessment tools (Static-99, Static-99R, Static-2002 and Static-2002R). The distribution of Static-99 scores was highly stable in international comparisons of sexual offenders from Canada (1990 to 2005; n = 2,011), Sweden (1993 to 1997; n = 1,278) and California (2008 to 2010; n = 37,600). The major limitation of percentile ranks is that they measure the “unusualness” of scores in a particular reference group, and may not correspond to other indicators of relative or absolute risk. Consequently, we recommend that evaluators presenting percentile ranks should consistently provide recidivism base rate information so that decision makers do not confuse the rarity of a score with estimates of absolute recidivism risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".