What risk assessment tools can be used with men convicted of child sexual exploitation material offenses? Recommendations from a review of current research.
Bibliographic record
Abstract
OBJECTIVE: We aimed to review research on recidivism risk assessment tools with individuals convicted of child sexual exploitation material (CSEM) offenses and make recommendations for use in forensic, correctional, and legal settings. HYPOTHESES: Multiple tools would be defensible to use with individuals convicted of CSEM offenses. METHOD: We discuss a minimum threshold of predictive accuracy to justify using a risk tool as an improvement on the typical level of accuracy expected from unstructured professional judgment. Beyond this minimum threshold, we offer additional considerations that researchers and practitioners can use in evaluating and selecting risk tools. RESULTS: We identified nine risk assessment tools with predictive accuracy research on individuals convicted of CSEM offenses: Child Pornography Offender Risk Tool (CPORT), Risk Matrix 2000/Sex (RM2000/S), OASys Sexual Reoffending Predictor-Indecent Images (OSP/I), Static-99R, STABLE-2007, ACUTE-2007, Post Conviction Risk Assessment (PCRA), Level of Service Inventory-Ontario Revision (LSI-OR), and Offender Group Reconviction Scale 3 (OGRS3). CONCLUSION: The CPORT, RM2000/S, STABLE-2007, and ACUTE-2007 (in conjunction with the STABLE-2007) are all defensible tools to use for assessing risk of any sexual recidivism or CSEM recidivism, specifically. The OSP/I consists of a single risk factor and considers risk of CSEM recidivism among all individuals convicted of sexual offenses, not only among individuals convicted of CSEM offenses. There is some support for Static-99R and the OGRS3, but they are not recommended options at this time, for different reasons. The PCRA and LSI-OR general recidivism risk tools have some empirical support in predicting general recidivism among CSEM samples (and sexual recidivism for the PCRA), with limitations noted. The use of multiple tools may have value in assessing risk and structuring management in CSEM cases; however, how they are best combined for these samples is still unclear. We expect research in this area to increase rapidly. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".