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Record W3011042232 · doi:10.1111/cogs.12824

Young Children Selectively Hide the Truth About Sensitive Topics

2020· article· en· W3011042232 on OpenAlex
Gail D. Heyman, Xiao Pan Ding, Genyue Fu, Fen Xu, Brian J. Compton, Kang Lee

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCognitive Science · 2020
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSalientTruth tellingPsychologySocial psychologyPsychoanalysisComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Starting in early childhood, children are socialized to be honest. However, they are also expected to avoid telling the truth in sensitive situations if doing so could be seen as inappropriate or impolite. Across two studies (total N = 358), the reasoning of 3- to 5-year-old children in such a scenario was investigated by manipulating whether the information in question would be helpful to the recipient. The studies used a reverse rouge paradigm, in which a confederate with a highly salient red mark on her nose asked children whether she looked okay prior to having her picture taken. In Study 1, children tended to tell the truth only if they were able to observe that the mark was temporary and the confederate did not know it was there. In Study 2, children tended to tell the truth only if they were able to observe that the mark could be concealed with makeup. These findings show that for children as young as age 3, decisions about whether to tell the truth are influenced by the likelihood that the information would be helpful to the recipient.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.023
GPT teacher head0.292
Teacher spread0.269 · 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