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Reasoning about modesty among adolescents and adults in China and the U.S.

2010· article· en· W1982597570 on OpenAlexaff
Genyue Fu, Gail D. Heyman, Kang Lee

Bibliographic record

VenueJournal of Adolescence · 2010
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Toronto
FundersNational Institute of Child Health and Human DevelopmentEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentZhejiang UniversityZhejiang Normal University
KeywordsLyingPsychologyDeedSocial psychologyCollectivismContext (archaeology)Moral reasoningChinaIndividualismDevelopmental psychologyLaw

Abstract

fetched live from OpenAlex

Reasoning about modesty was examined among adolescents and young adults in China and the U.S. Participants made moral judgments of story characters who did a good deed and either truthfully accepted credit for it, or falsely denied having done it. The social context in which statements occurred was manipulated, with some made in private and others in front of a class. Chinese participants judged accepting credit for good deeds less favorably and lying in the service of modesty more favorably than did participants from the U.S. In each country, older participants judged modesty-based lies more favorably when they were told in public. Additionally, a high collectivist orientation and low individualistic orientation was associated with higher ratings of modesty-based lying in public, which provides the first direct link between endorsement of these values and moral judgments about lie-telling.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.244
Teacher spread0.228 · 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

Citations42
Published2010
Admission routes1
Has abstractyes

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