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Record W4388129716 · doi:10.1177/00938548231208194

The CPORT and Risk Matrix 2000 for Men Convicted of Child Sexual Exploitation Material (CSEM) Offenses: A Predictive Accuracy Comparison and Meta-Analysis

2023· article· en· W4388129716 on OpenAlexaff
L. Maaike Helmus, Angela W. Eke, Linda Farmus, Michael C. Seto

Bibliographic record

VenueCriminal Justice and Behavior · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsRoyal Ottawa Mental Health CentreYork UniversityGovernment of OntarioSimon Fraser University
Fundersnot available
KeywordsRecidivismRisk assessmentSex offensePoison controlChild pornographyPsychologyInjury preventionDemographyClinical psychologyMedicineSexual abuseMedical emergencyComputer securityComputer scienceThe Internet

Abstract

fetched live from OpenAlex

There is demand for valid risk assessment of individuals with child sexual exploitation material (CSEM) offenses. We compared the predictive performance of the Risk Matrix 2000/Sex (RM2000/S) and the Child Pornography Offender Risk Tool (CPORT) among 365 men convicted of CSEM offenses. In fixed 5-year follow-up analyses, the CPORT (area under the curve [AUC] = .73) had significantly higher predictive accuracy than the RM2000/S (AUC = .66) for any sexual recidivism. The predictive difference for CSEM recidivism was not statistically significant. A meta-analysis found the CPORT had large effects in predicting sexual recidivism (AUC = .75) and moderate accuracy for CSEM recidivism (AUCs = .65 and .66), while the RM2000/S had moderate accuracy in predicting any sexual recidivism (AUC = .66; insufficient studies of CSEM recidivism). Results suggest a tool developed specifically for CSEM offending, such as CPORT, may perform better at predicting any sexual recidivism than adapting a general sexual offending risk tool.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.093
GPT teacher head0.394
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2023
Admission routes1
Has abstractyes

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