Cultural divergence in children's selective word learning: Korean and Canadian children differ in their trust of adult informants
Bibliographic record
Abstract
Abstract Although children generally regard adults as more knowledgeable than their peers, an informant's past accuracy trumps age when in conflict. In a recent study, however, Korean 5‐year‐olds were more likely to trust a less accurate adult informant over a more accurate peer informant when learning new information. To examine whether such a pattern was attributable to the cultural influence of shaping early respect for the elderly among Korean children, a pattern of selective label endorsement was examined among Canadian 5‐year‐olds, who were raised under different cultural values, relatively putting less emphasis on social relationships than individual expressions (Experiment 1). We also investigated Korean 6–7‐year‐olds’ selective endorsement pattern when the informant's past accuracy conflicted with the informant's age to examine how cultural influences shift as children develop (Experiment 2). When the adult was 25% accurate in labeling familiar objects, relative to the 75% accurate child informant, Canadian 5‐year‐olds tended to endorse the label offered by the child, demonstrating a prioritization of the epistemic cue over the social cue. By comparison, Korean 6–7‐year‐olds were equally likely to choose between two informants, showing difficulty disregarding inaccurate adults, even when they always mislabeled familiar objects. These results offer insight into cultural influences on the development of selective word learning and suggest the relative weighing of social and epistemic cues evolves with development.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".