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Record W2408040638 · doi:10.1111/cdev.12494

Learning to Be Unsung Heroes: Development of Reputation Management in Two Cultures

2016· article· en· W2408040638 on OpenAlexafffundabout
Genyue Fu, Gail D. Heyman, Catherine Ann Cameron, Kang Lee

Bibliographic record

VenueChild Development · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of TorontoUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentSocial Sciences and Humanities Research Council of CanadaNational Natural Science Foundation of China
KeywordsProsocial behaviorSocializationReputationPsychologyChinaSocial psychologyBridge (graph theory)Developmental psychologyChild developmentSociologyLawSocial sciencePolitical science

Abstract

fetched live from OpenAlex

The effective management of one's reputation is an important social skill, but little is known about how it develops. This study seeks to bridge the gap by examining how children communicate about their own good deeds, among 7- to 11-year-olds in both China and Canada (total N = 378). Participants cleaned a teacher's messy office in her absence, and their responses were observed when the teacher returned. Only the Chinese children showed an age-related increase in modesty by choosing to falsely deny their own good deeds. This modest behavior was uniquely predicted by Chinese children's evaluations of modesty-related lies. The results suggest that culture-specific socialization processes influence the way children communicate with authority figures about prosocial deeds.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.595
Threshold uncertainty score0.337

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 science0.0000.000
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 head0.025
GPT teacher head0.360
Teacher spread0.335 · 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 designNot applicable
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

Citations58
Published2016
Admission routes3
Has abstractyes

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