Testing the predictive utility of the STATIC‐99: A Bayes analysis
Bibliographic record
Abstract
Purpose. This study applies a Bayes analysis to the probability of a particular STATIC‐99 score and its associated re‐offence probability with recidivating for a sexual offence. We examine this probability over three time frames: within 5 years; within 10 years; and within 15 years. Methods. This study was conducted using the same data from . This dataset is constituted from four different samples: Institut Philippe Pinel (Canada) sample; Millbrook Recidivism Study (Canada) sample; Oak Ridge Division of the Penetanguishene Mental Health Center (Canada) sample; and Her Majesty's Prison Service (UK) sample. The final sample for which sufficient information was available to score the STATIC‐99 includes 1,086 sexual offenders. Bayes statistic has been used to analyse the data. Results. Results are consistent with the STATIC‐99 as a useful assessment tool. The Bayes‐generated probabilities as well as odds ratios show a consistent increase in increased likelihood of re‐offence as the score value increases. Conclusions. The Bayesian analysis of the STATIC‐99 shows that this method is very interesting in the context of risk assessment tools. This approach to risk assessment instruments may be more appropriate in the communication of analytic results as it can offer clinicians a combination of probabilities and likelihood ratios resulting a readily accessible profile of risk.
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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 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".