What Risk Assessment Tools can be Used With Men Convicted of Child Sexual Exploitation Material (CSEM) Offenses? Recommendations From a Review of Current Research
Bibliographic record
Abstract
Objective: Review current research on risk assessment tools with individuals convicted of child sexual exploitation materials (CSEM) offenses with recommendations for use in forensic, correctional, and legal settings. Hypotheses: Multiple tools would be defensible to use with individuals convicted of CSEM offenses. Methods: 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. Then beyond this minimum threshold, we offer additional considerations that researchers and practitioners can use in evaluating and selecting risk tools. Results: We identified eight risk assessment tools with predictive accuracy research on individuals convicted of CSEM offenses: the 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), and the Level of Service Inventory – Ontario Revision (LSI-OR). We review each using the evaluation considerations. Conclusions: The CPORT, RM2000/S, STABLE-2007, and ACUTE-2007 (in conjunction with the STABLE) are all defensible tools to use for assessing risk of any sexual recidivism or CSEM recidivism specifically. There is preliminary evidence suggesting some support for Static-99R, but it may not be the ideal choice. 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. The PCRA and LSIOR 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 continue to build rapidly.
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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 | 0.036 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".