Initial Denial of Child Sexual Abuse: Reluctance or Suggestibility
Bibliographic record
Abstract
We review the research examining disclosure rates in child suspected of being sexually abused and show that a substantial percentage of sexually abused children deny abuse when initially questioned. To adequately interpret these studies, however, we confront several methodological problems. First, there is the ground truth problem: How do we know whether abuse actually occurred? Second, there are selection bias problems. This includes substantiation selection bias and substantiation suspicion bias: To the extent that abuse is suspected and substantiated because of disclosure, samples of children questioned about suspected abuse will have inflated rates of disclosure. We can reduce these problems somewhat by examining cases with corroborative evidence of abuse. But this raises the third problem. The apparent rate of disclosure among abused children will be inflated in corroborated cases if corroboration is dependent on disclosure. Solving the methodological problems reveals that a substantial percentage of sexually abused children deny abuse when first questioned. The research supports expert testimony explaining to jurors how abused children are frequently reluctant to disclose abuse, and calls into question the external validity of suggestibility research, challenging expert claims that false allegations in lab research translate into false disclosures of sexual abuse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".