Correspondence of Child Age and Gender Distribution in Child Sexual Exploitation Material and Other Child Content With Age and Gender of Child Sexual Assault Victims
Bibliographic record
Abstract
in legal statutes) can indicate sexual interest in children. It logically follows then that the age and gender of the depicted children may reflect specific interests in those age/gender groups, and if so, may correspond to age and gender of any known contact offending victims. We had data on CSEM characteristics and child victims for 71 men convicted of CSEM offenses who also had contact sexual offenses against children; some had also sexually solicited children online. Sixty-four men had 134 prior or concurrent child victims, and 14 men reoffended directly against 17 children during follow-up. There were significant, positive associations (with moderate to large effect sizes) between age and gender of children depicted in CSEM and age and gender of child contact or solicitation victims. Examining future offending, though with only 14 recidivists, all men who sexually reoffended against a girl had more girl CSEM content, and all men who sexually reoffended against a boy had more boy CSEM content. Our results suggest that CSEM characteristics can reflect child preferences. This information can be relevant in clinical settings, police investigations, and community risk management, though it does not rule out interest in, or offending against, victims of other ages or gender. We discuss these findings in the context of other evidence regarding victim cross-over, and suggest future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".