Is the Static-99R valid for all men with ‘identifiable’ victims? Examining cases of online sexual solicitation of children
Bibliographic record
Abstract
This study assessed the predictive validity of the Static-99R among 172 men convicted of online sexual solicitation of minors in Canada – 93 from clinical settings and 79 from police investigations. It examined recidivism rates for sexual (any, contact, and child luring) and nonsexual violent offenses, comparing men with and without intent to engage in contact sexual offenses. Results showed the Static-99R effectively predicted recidivism, with modest to strong accuracy for contact sexual recidivism (Harrell's C s = 0.78–0.94), any sexual recidivism (Harrell's C s = 0.66–0.80), and child luring-specific recidivism (Harrell's C s = 0.61–0.71). Predictive validity was consistent regardless of offenders' intent to engage in contact sexual offenses. Calibration analyses indicated the Static-99R underestimated the number of sexual recidivists in our sample. The study also highlighted differences between online and offline offenders, noting that online offenders often had numerous victims, which could complicate risk assessments. Discussion includes the relevance of Static-99R items related to victim characteristics and the need to refine tools for online offenders. Incorporating unique factors of online offenses may enhance the tool's predictive validity and practical application. Future research should address these dynamics to improve risk assessment for online sexual offenders.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".