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Record W2883954943 · doi:10.1111/sode.12327

Modesty can promote trust: Evidence from China

2018· article· en· W2883954943 on OpenAlexaff
Fengling Ma, Gail D. Heyman, Xiao Li, Fen Xu, Brian J. Compton, Kang Lee

Bibliographic record

VenueSocial Development · 2018
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Toronto
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsTrustworthinessPromotion (chess)ChinaPsychologySocial psychologyValue (mathematics)LawPolitical science

Abstract

fetched live from OpenAlex

Abstract When people let others know about their accomplishments, they can improve their social standing, but doing so may also have a cost, especially within social environments in which there is great emphasis on the value of modesty. One particular downside of self‐promotion, the risk of being seen as untrustworthy, was examined among children in China. Across three studies, children ranging in age from 7 to 11 years (total N = 251) judged the trustworthiness of protagonists who exhibited either modesty or immodesty. In Study 1, protagonists who told lies in the service of modesty were judged as more trustworthy than those who told lies to avoid getting into trouble. In Study 2, protagonists who demonstrated modesty were rated as trustworthy, but those who demonstrated immodesty were not. Study 3 showed that the positive implications of modesty for trust are specific to downplaying one’s own accomplishments and do not extend to downplaying the accomplishments of a peer. Taken together, the results suggest that for children in China, the level of modesty serves as a cue about which people can be trusted.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.111
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.003
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.146
GPT teacher head0.312
Teacher spread0.166 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2018
Admission routes1
Has abstractyes

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