Views and Attitudes about Youth Self-Produced Sexual Images among Professionals with Expertise in Child Sexual Abuse
Bibliographic record
Abstract
The proliferation of youth self-produced sexual images (SPSI) raises complex practice challenges for professionals supporting victims of sexual abuse. This paper examines the views and perspectives about youth SPSI among professionals with expertise in child sexual abuse, who in the course of a study on child sexual abuse images, commonly raised SPSI without prompting. Eighty-four participants from three professional sectors (Internet child exploitation law enforcement, child protection, and children's mental health) took part in 12 focus groups, the analysis of which indicates that most participants regarded youth SPSI as a complex social phenomenon, and had trouble fitting it into their existing professional expertise. Participants were immersed in larger cultural narratives about youth sexual agency, the dangers of constantly evolving technology, and the digital age compounding generational differences. Lack of clarity about when and whether a young person requires support and/or legal intervention arose from a tangled web of punitive, permissive, and ambivalent perspectives on youth SPSI. Professionals experienced with victims of sexual abuse focused on SPSI as opposed to child abuse images, and struggled to distinguish between what is normal versus problematic youth sexuality in the digital age, confounding efforts to settle on appropriate legal and support responses and interventions.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".