Viewing Child Sexual Abuse Material for the First Time: Findings From an Anonymous Survey of Internet Users
Bibliographic record
Abstract
The number of reports of child sexual abuse material (CSAM) detected on online platforms has increased dramatically in the last decade. Research has suggested that some individuals engage in a progression from typical adult pornography to atypical adult pornography (e.g., bondage, discipline, sadism, and masochism (BDSM), bestiality) to CSAM. Examining the onset to adult pornography and CSAM can therefore help identify intervention points for prevention and disruption. To investigate first exposure to adult pornography and CSAM, we anonymously surveyed a community sample of 5512 adults in five different countries: 742 (13.5%) survey participants self-reported viewing CSAM; 77% were male, 19.5% were female and 3.5% identified as another gender/sex. Majorities of respondents who viewed CSAM (71.2%), BDSM adult pornography (66.6%), and bestiality adult pornography (62.4%) were first exposed to this material prior to age 18. Females were significantly more likely than males to view adult content at younger ages. Younger age of exposure to typical and atypical adult pornography predicted younger age of exposure to CSAM, and respondents aged under 40 years were significantly more likely to view CSAM at younger ages ( p < .001). Self-reported CSAM viewers predominantly first discovered CSAM unintentionally (86.1%) and when alone (76%). The findings suggest a need for increased interventions that prevent exposure to CSAM and illegal adult content among adolescents.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".