Boys Abused in a Community Setting: An Analysis of Gender, Relationship, and Delayed Prosecutions in Cases of Child Sexual Abuse
Bibliographic record
Abstract
Previous research on gender differences and delay of disclosure of child sexual abuse is inconclusive; some research has found that male victims are more likely to delay disclosure than female victims, while other studies have found no gender difference. The present archival study investigated this inconsistency by examining factors that interact with delay. Judicial outcomes of child sexual abuse cases were coded (N = 4237) for variables related to the offense, the complainant-accused relationship, and court proceedings. Males and females differed with respect to delay only when the relationship between the complainant and the accused was established in the community (e.g., sports coach) or was a stranger to the child. When the accused was a parent, other relative, or a non-relative connected to the child through the family, there was no difference in delay between males and females. Further, males were more vulnerable in the community, as evidenced by a higher proportion of accused community members with male than female complainants, even though males represented fewer than 25% of cases in the database. These findings may help explain inconsistencies in gender differences in delayed disclosure. Implications regarding education about child sexual abuse are discussed, with a focus on male victims.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".