Developing Nonarbitrary Metrics for Risk Communication
Bibliographic record
Abstract
Nominal risk categories for actuarial risk assessment information should be grounded in nonarbitrary, evidence-based criteria. The current study presents numeric indicators for interpreting one such tool, the Risk Matrix 2000, which is widely used to assess the recidivism risk of sexual offenders. Percentiles, risk ratios, and 5-year recidivism rates are presented based on an aggregated sample ( N = 3,144) from four settings: England and Wales, Scotland, Germany, and Canada. The Risk Matrix 2000 Sex, Violence, and Combined scales showed moderate accuracy in assessing the risk of sexual, non-sexual violent, and violent recidivism, respectively. Although there were some differences across samples in the distributions of risk categories, relative increases in recidivism for ascending risk categories were remarkably consistent. Options for presenting percentiles, risk ratios, and absolute recidivism estimates in applied evaluations are offered, with discussion of the advantages, disadvantages, and limitations of these risk communication metrics.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".