How well do indirect measures assess sexual interest in children? A meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: We quantitatively reviewed the construct validity evidence for all cognitively based indirect measures of sexual interest in prepubescent children (pedophilic interest) and pubescent children (hebephilic interest) using meta-analysis. METHOD: Studies were included if they presented scores on a cognitively based indirect measure of pedohebephilic interest for a sample of adolescent or adult males who had committed a sexual offense against a child 16 years of age or younger, or who reported sexual interest in children, and for a comparison group. Studies were also included if they reported on the strength of association between scores on an indirect measure and an independent indicator of pedohebephilic interest in a sample of males. We used meta-analysis with robust variance estimation to summarize effect sizes and metaregression to test potential moderators. RESULTS: Cognitively based indirect measures of pedohebephilic interest showed a moderate difference between pedohebephilic (n = 2,552) and nonpedohebephilic males (n = 2,434), d = 0.61, 95% CI [0.46, 0.76], k = 39. A small-to-moderate correlation was also observed between indirect measures and independent indicators of pedohebephilic interest, r = .23, 95% CI [0.17, 0.28], k = 23, n = 3,623. These effects were qualified by substantial heterogeneity; however, most moderators we tested did not account for a significant amount of heterogeneity. CONCLUSIONS: Findings suggest that publication bias did not substantially distort the results. However, the lack of significant moderators suggests more research is needed to understand the conditions under which indirect measures best reflect pedohebephilic interest. (PsycInfo Database Record (c) 2021 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 | 0.043 | 0.101 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.054 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".