ACUTE-2007 and STABLE-2007 predict recidivism for men adjudicated for child sexual exploitation material offending.
Bibliographic record
Abstract
OBJECTIVE: Risk assessment is essential to effective correctional practice. For individuals with contact sexual offenses, many risk tools are available. There are fewer options, however, for individuals whose sexual offending exclusively involves child sexual exploitation materials (CSEM; legally referred to in Canada and the United States as child pornography). HYPOTHESES: The present study examined the predictive validity of the ACUTE-2007 and STABLE-2007 sexual recidivism risk tools among men with CSEM offenses. We expected these tools to show moderate predictive validity across study groups. METHOD: We compared the scales' discrimination and calibration across three groups: (a) 1,042 men with contact sexual offenses against children (baseline comparison), (b) 228 men with exclusive CSEM offending (no contact sexual offenses), and (c) 80 men with both contact sexual offenses and CSEM offenses. RESULTS: We found that the ACUTE-2007 and STABLE-2007 total scores and items had comparable (and often better) discrimination for men with CSEM offending compared with contact sexual offending against children in the prediction of any sexual recidivism, violent recidivism, and any recidivism. Calibration analyses indicated that the overall sexual recidivism rates for the median ACUTE-2007 and STABLE-2007 scores were similar for men with exclusive CSEM offenses compared with men with any contact offending against children. Almost all of the sexual recidivism for the CSEM-exclusive group involved further CSEM offenses. CONCLUSIONS: This study supports the use of these tools to rank-order men with CSEM offending in terms of their risk of reoffending and to help direct treatment and management efforts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".