The effects of different paraphrasing styles on the quality of reports from young child witnesses
Bibliographic record
Abstract
Young children's descriptions of sexual abuse are often sparse thus creating the need for techniques that elicit lengthier accounts. ‘Paraphrasing’, or repeating information children have just disclosed, is a technique sometimes used by forensic interviewers to clarify or elicit information (e.g. if a child stated ‘He touched me’, an interviewer could respond ‘He touched you?’). However, the effects of paraphrasing have yet to be scientifically assessed. The impact of different paraphrasing styles on young children's reports was investigated. Overall, paraphrasing per se did not improve the length, richness, or accuracy of reports when compared to open-ended prompts such as ‘tell me more’, but some styles of paraphrasing were more beneficial than others. The results provide clear recommendations for investigative interviewers about how to use paraphrasing appropriately, and which practices can compromise the quality of children's reports.
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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 | 0.038 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".