Questioning the expertise of “experts” who recommend active hypothesis testing in child forensic interviews
Bibliographic record
Abstract
Abstract The McMartin Preschool case in the 1980s was one of several alleged day care sex abuse cases that highlighted the need to establish best practices in forensic child interviews. As a result of that case, tens of millions of dollars were allocated to study and develop child interviewing techniques that were most likely to preserve the integrity of the accounts of children identified as possible victims and survivors of child abuse. That research led to the identification and standardization of the child forensic interviewing techniques widely used today. Despite the creation of best practices for child forensic interviews, courts sometimes accept as expert witnesses professionals who promote practices that should not be used when interviewing children who may have been victimized. A recent case in Canada underscores the critical need for awareness of best practices in child forensic interviews. This discussion article examines the dangers of viewing the lack of active hypothesis testing as a sign of poor interviewing.
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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 | 0.390 | 0.640 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".