When children can explain why they believe a claim, they suggest a better empirical test for that claim
Bibliographic record
Abstract
We tested the hypothesis that children’s ability to reflect on the causes of their uncertainty about a surprising claim allows them to better target their empirical investigation of that claim—and that this ability increases with age. We assigned 4–7-year-old children ( n =174, M age = 68.77 months, 52.87% girls) to either a prompted or an unprompted condition. In each condition, children witnessed a series of vignettes where an adult presented a surprising claim about an object. Children were then asked whether they thought the claim was true or not, how certain or uncertain they were, and how they would test that claim. In the prompted condition, children were also asked why they were certain or uncertain. As predicted, older children were more likely to justify their beliefs and to suggest targeted empirical tests, compared with younger children. Being prompted to reflect on their uncertainty did not increase children’s ability to generate an efficient test for those claims. However, exploratory analyses revealed that children’s ability to provide a plausible reason for their beliefs did, controlling for their ability to select an efficient test for a claim. This suggests that developments in children’s reasoning about their beliefs allow them to more effectively assess those beliefs empirically.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".