How Do Interviewers and Children Discuss Individual Occurrences of Alleged Repeated Abuse in Forensic Interviews?
Bibliographic record
Abstract
Summary Police interviews (n = 97) with 5‐ to 13‐year‐olds alleging multiple incidents of sexual abuse were examined to determine how interviewers elicited and children recounted specific instances of abuse. Coders assessed the labels for individual occurrences that arose in interviews, recording who generated them, how they were used and other devices to aid particularisation such as the use of episodic and generic language. Interviewers used significantly more temporal labels than did children. With age, children were more likely to generate labels themselves, and most children generated at least one label. In 66% of the cases, interviewers ignored or replaced children's labels, and when they did so, children reported proportionately fewer episodic details. Children were highly responsive to the interviewers' language style. Results indicate that appropriately trained interviewers can help children of all ages to provide the specific details often necessary to ensure successful prosecution. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.019 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".