On the Assessment of Children in Suspected Child Sexual Abuse in Light of Daubert and Frye: Limitations of Profiles and Interviews as Scientifically Grounded Evidence
Bibliographic record
Abstract
Practice with children and families entails the higher probability of encountering forensic issues of child sexual abuse (CSA) assessments for which relatively few psychologists, allied mental health and legal practitioners are sufficiently well equipped. The current paper reviews some of the key psycholegal issues bearing on the assessment of suspected CSA in the contexts of: (a) recent psycholegal precedence and common law rules of reliability and admissibility of CSA profile evidence; (b) the empirical problems with CSA syndromes; and (c) the problems with children's interviews as evidence, and suggestions for valid interviewing guidelines supporting free recall. These psycholegal issues are presented in terms of the Frye standard for expert testimony and the Federal Rules of Evidence, with recent American and Canadian case illustrations, such as Daubert v. Merrell Dow Pharmaceuticals, Hadden v. State of Florida (1997), Bighead v. The United States of America (1997), Diocese of Winona v. Interstate Fire & Cas. Co. (1994), and R. v. Simpson (1996).
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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 | 0.089 | 0.188 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".