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Record W4414396269 · doi:10.1111/bjdp.70018

The impact of well‐groomed appearance on children's epistemic trust decision

2025· article· en· W4414396269 on OpenAlexaff
Hüseyin Kotaman, Aslı Balci

Bibliographic record

VenueBritish Journal of Developmental Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsCompetence (human resources)Theory of mindPerspective-takingInformation seekingReliability (semiconductor)

Abstract

fetched live from OpenAlex

This study investigates whether children's trust in information sources is influenced by the groomed or ungroomed appearance of an individual and whether age affects this decision-making process. A total of 662 children aged 4-10 from kindergarten, second grade and fourth grade participated. Children viewed photos of identical twins labelled as groomed or ungroomed, then watched videos where these individuals named unfamiliar shapes. Results showed that children across all age groups significantly preferred the groomed individual. Analysis of their explanations revealed that kindergarteners relied more on appearance-based justifications compared with older children. From second grade onward, children increasingly shifted towards accuracy- and skill-based explanations, even without direct evidence of competence. This developmental trend suggests that as children's language and reasoning abilities improve, they begin to provide more epistemic justifications rather than relying on superficial cues. Overall, the findings indicate that appearance strongly affects children's epistemic trust decisions throughout early and middle childhood. The study highlights the importance of educational practices that help children critically evaluate information sources based on reliability and competence rather than external appearance.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.361
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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