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Record W4240466408 · doi:10.32920/ryerson.14651946.v1

Young Children’s Trust in the False Testimony of ingroup versus outgroup speakers

2021· preprint· en· W4240466408 on OpenAlexaff
Kyla P. McDonald

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsTrent University
Fundersnot available
KeywordsIngroups and outgroupsPsychologyOutgroupSkepticismSocial psychologyStress (linguistics)Developmental psychologyTheory of mindLinguisticsCognition

Abstract

fetched live from OpenAlex

The present research explores whether young children display different levels of trust in the testimony of speakers from their own social group (ingroups) versus another social group (outgroups). Three- and 4-year-old children watched through a window as an adult hid a toy in one of three containers. The adult then told the child that she had put the toy in a container different from the one where it was actually hidden (i.e., false testimony). At the end the child was asked to retrieve the toy. The adult was either a Caucasian, native English speaker ingroup) or an Asian English speaker with a noticeable foreign accent (outgroup). Four-year-old children were credulous to the false testimony of the ingroup speaker, despite their firsthand observations, but were skeptical and relied on their own observations when the false testimony was provided by the outgroup speaker. In contrast, 3-year-old children remained credulous to the false testimony of both speakers. These findings were discussed in relation to children’s early preferences for ingroup members and the developmental shift in skepticism displayed by 4-year-old, but not 3-year-old children. This research will make a unique contribution to our understanding of how young children selectively learn from other people and why they remain credulous to some speakers, but not to others.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.065
GPT teacher head0.297
Teacher spread0.232 · 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.

Study designQualitative
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
Published2021
Admission routes1
Has abstractyes

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