Encouraging more open-ended recall in child interviews
Bibliographic record
Abstract
The goal of child forensic interviewers is to obtain as much information as possible through open-ended recall. Unfortunately, typically interviewers quickly switch to focused questions. This article suggests a way of eliciting more open-ended recall by using the narrative elaboration (NE) procedure, which includes four initial prompts about event participants, context, actions, conversations, and thoughts. The procedure uses line drawings on cards as prompts and requires pre-training; although it substantially increases open-ended recall, in practice it is too time-consuming for regular use. The original NE procedure is compared with two streamlined versions with 3- to 7-year-olds: using NE cards with no pre-training and simply providing parallel NE verbal prompts without using the cards. The children in the streamlined NE interview with verbal prompts were found to provide as much additional information as those in the full NE interview, and considerably more than those in the control interview. Therefore, incorporating NE verbal prompts near the beginning of child interviews is an easy way to increase the amount of information that children provide in open-ended recall.
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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 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".