Encouraging children's clarification requests with “I don't understand” rule reminders
Bibliographic record
Abstract
Abstract When children are questioned, it is crucial they request clarification to resolve potential misunderstandings. The current research tested a method for increasing children's appropriate clarification requests during an interview, and examined the impact of age and question characteristics. Children ( n = 81), ages 6‐ to 11‐years‐old, responded to scripted questions, some of which were designed to be “tricky” and required clarification. Half of the children received “I don't understand” (IDU) rule reminders during the interview. Older children and children who received IDU rule reminders requested clarification to a significantly greater proportion of tricky questions than younger children and children who did not receive reminders. Results indicate that children can recognize when they need clarification, and reminding them of the IDU rule increases the frequency with which they request clarification. Children's ability to request clarification provides insight into children's metacognitive abilities and has implications for those who question children across contexts (e.g., forensic, research).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".