Correlates of admitted sexual interest in children among individuals convicted of child pornography offenses.
Bibliographic record
Abstract
Recent research on a risk assessment tool for child pornography offending suggests that admission of sexual interest in children is a risk factor for any sexual recidivism. Admission is easily vulnerable to lying, however, or to refusals to respond when asked about sexual interests. This may become a particular issue when individuals are concerned about the potential impact of admission of sexual interest on sentencing and other risk-related decisions. In this study, we identified the following behavioral correlates (coded yes/no) of admission of sexual interest in children in the risk tool development sample of 286 men convicted of child pornography offenses: (a) never married (54% of sample), (b) child pornography content included child sexual abuse videos (64%), (c) child pornography content included sex stories involving children (31%), (d) evidence of interest in child pornography spanned 2 or more years (55%), (e) volunteered in a role with high access to children (7%), and (f) engaged in online sexual communication with a minor or officer posing as a minor (10%). When summed, the average score on this Correlates of Admission of Sexual Interest in Children (CASIC) measure was 2.21 (SD = 1.22, range 0-6) out of a possible 6, and the CASIC score was significantly associated with admission of sexual interest in children, area under the curve (AUC) = .71, 95% CI [ .65, .77]. The CASIC had a stronger relationship with admission in a small cross-validation sample of 60 child pornography offenders, AUC = .81, 95% CI [.68, .95]. CASIC scores may substitute for admission of sexual interest in risk assessment involving those with child pornography offenses. (PsycINFO Database Record
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".