Predictive Validity of Static-99R Among 8,207 Men Convicted of Sexual Crimes in South Korea: A Prospective Field Study
Bibliographic record
Abstract
The accuracy of risk assessment tools for Asian populations has received relatively little research attention. This study evaluated one of the most widely used static risk assessment tools - Static-99R - for assessing the likelihood of recidivism among men convicted of a sexual crime in South Korea. Overall, this South Korean sample ( N = 8207) appeared to have a higher risk (more paraphilic interests, more sexual/general criminality) than the Static-99R normative samples (who were mostly White individuals from Western countries). Despite the differences, Static-99R was able to discriminate recidivists from nonrecidivists in South Korea, with AUC values similar to that observed in the normative samples (e.g., 0.72 for sexual recidivism). In terms of calibration, the observed sexual recidivism rates of the current sample were higher than the international routine/complete normative samples but lower than the high-risk/high-need normative samples ( E/ O = 0.75 and 1.26, respectively). Consequently, evaluators in South Korea can have reasonable confidence in the ability of Static-99R to rank individuals according to their relative likelihood of sexual recidivism.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".