Children's clarification requests in interviews: Testing the effects of age, question characteristics, and brief intervention strategies
Bibliographic record
Abstract
Abstract In some contexts (e.g., legal and medical), it is imperative that children indicate when they do not understand an adult's question. Yet, little research has examined children's clarification requests. We asked 122 4‐ to 9‐year‐olds tricky and simple interview questions to assess how often and how children request clarification in interviews, the factors associated with these requests, and whether brief interventions that supplement standard ground rule instructions increase such requests. Overall, the majority of children requested clarification at least once, and most did so explicitly. Child age and question characteristics appeared to influence such requests. Questions that were tricky, especially those that were inaudible or included complex vocabulary/syntax, elicited more clarification requests than simple questions. Supplementing standard ground rule instructions with additional explanations failed to significantly increase clarification requests, though teaching children a clear method for requesting clarification had limited effects on their responses. Results provide insight into children's cognitive development and have implications for interviewing children.
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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 | 0.028 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".