The Prevalence of Sexual Interest in Children and Sexually Harmful Behavior Self-Reported by Men Recruited Through an Online Crowdsourcing Platform
Bibliographic record
Abstract
This study examined the feasibility of using crowdsourcing to recruit men who self-report sexual interest in children or sexually problematic behavior involving children. Crowdsourcing refers to the use of the internet to reach a large number of people to complete a specific task. A nonrepresentative sample of men ( N = 997) participated in a brief self-report survey examining age of attraction, sexual interest in children, proclivity toward sexual offenses involving children, and history of sexual offending. Almost a quarter of the sample (23.1%) indicated some degree of sexual interest in children, propensity to sexually offend against children, and/or actual offending behavior. We present our data broken down by type of interest or behavior and examine the frequency of these outcomes. Findings are likely to be of value to those considering the viability of crowdsourcing to overcome the limitations or challenges of face-to-face research on stigmatizing interests and behaviors. Findings also contribute to estimating prevalence of self-reported sexual interest in children, and sexual offending behavior toward children, across different countries.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".