Older children verify adult claims because they are skeptical of those claims
Bibliographic record
Abstract
We investigated children's information seeking in response to a surprising claim (Study 1, N = 109, 54 Female, Range = 4.02-6.94 years, 49% White, 21% Mixed Ethnicity, 19% Southeast Asian, September 2019-March 2020; Study 2, N = 154, 74 Female, Range = 4.09-7.99, 50% White, 20% Mixed Ethnicity, 17% Southeast Asian, September 2020-December 2020). Relative to younger children, older children more often expressed skepticism about the adult's surprising claims (1-year increase, OR = 2.70) and more often suggested exploration strategies appropriate for testing the specific claim they heard (1-year increase, OR = 1.42). Controlling for age, recommending more targeted exploration strategies was associated with a greater likelihood of expressing skepticism about the adult's claim.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".