Child Witnesses Productively Respond to “How” Questions About Evaluations but Struggle With Other “How” Questions
Bibliographic record
Abstract
Child interviewers are often advised to avoid asking “How” questions, particularly with young children. However, children tend to answer “How” evaluative questions productively (e.g., “How did you feel?”). “How” evaluative questions are phrased as a “How” followed by an auxiliary verb (e.g., “did” or “was”), but so are “How” questions requesting information about method or manner (e.g., “How did he touch you?”), and “How” method/manner questions might be more difficult for children to answer. We examined 458 5- to 17-year-old children questioned about sexual abuse, identified 2485 "How” questions with an auxiliary verb, and classified them as “How” evaluative ( n = 886) or “How” method/manner ( n = 1599). Across age, children gave more productive answers to “How” evaluative questions than “How” method/manner questions. Although even young children responded appropriately to “How” method/manner questions over 80% of the time, specific types of “How” method/manner questions were particularly difficult, including questions regarding clothing, body positioning, and the nature of touch. Children’s difficulties lie in specific combinations of “How” questions and topics, rather than “How” questions in general.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".