An Examination of Children's Selective Social Learning Based on Expertise Cues
Bibliographic record
Abstract
At a young age, children develop the ability to make selective social learning decisions, wherein they decide from whom they trust to learn new information. Substantial literature has examined children's selective social learning decisions based on epistemic cues (e.g., knowledgeability) and non-epistemic cues (e.g., perceived benevolence). When these cues are pitted against each other, a shift emerges wherein children aged 6-8 tend to rely on epistemic cues whereas children aged 4-6 prioritize non-epistemic cues. As children encounter experts such as doctors from an early age, it is important to determine how they understand and recognize expertise, an epistemic cue, and the extent to which expertise guides their selective social learning decisions. This research aimed to address these questions. Study 1 (Experiments 1-3) showed that children aged 4-5 were able to recognize expertise when it was indicated by an explicit label and professional attire, and that they could determine relative expertise when a clear contrast was presented (e.g., one informant works in a given field whereas the other has no exposure to that field). These cues subsequently informed their selective social learning decisions. In contrast, they struggled to discern expertise when it was indicated using technical language, and when the degrees of expertise were increasingly nuanced (i.e., contrasting informants that have exposure to a field as a hobby and for work). Children aged 7-8 were able to infer expertise in medicine from technical language and make selective learning decisions accordingly. Study 2 (Experiments 4-5) examined the robustness of children’s preferences to learn from experts, and pitted expertise cues against group membership as indicated by nationality and the minimal group paradigm. Results revealed that 7- to 8-year-olds, but not 4- to 5-year-olds, prioritized expertise cues over shared group membership (i.e., ingroup information) in their selective learning decisions. These results provide novel insights into the developmental changes in children’s conceptions of expertise and how they prioritize different expertise cues to guide their social learning. The implications of this can be seen in children’s susceptibility to misinformation from others if their selective trust decisions are based on cues that are not reliable or valid.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".