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Record W2296943176 · doi:10.1177/1069397115617902

The Role of Positive Self-Evaluation on Cross-Cultural Differences in Well-Being

2015· article· en· W2296943176 on OpenAlexafffund
Hyunji Kim, Ulrich Schimmack, Cecilia Cheng, Gregory D. Webster, Aleksandr Spectre

Bibliographic record

VenueCross-Cultural Research · 2015
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Toronto
FundersFreie Universität BerlinUniversity of Hong KongYork University
KeywordsGeneralizability theoryPsychologySocial psychologyCross-culturalWell-beingLife satisfactionCross-cultural studiesSubjective well-beingChinaPersonalityTest (biology)Self-enhancementDemographyDevelopmental psychologyPolitical scienceSociologyHappiness

Abstract

fetched live from OpenAlex

Past studies have shown that North Americans have higher well-being compared with East Asians. Objective living conditions (e.g., wealth, education, personal and political freedom) have been found to substantially contribute to North Americans’ higher well-being. One other possible explanation is that North American culture fosters positive evaluations of the self to enhance self-esteem and to feel positive emotions, which may lead North Americans to provide favorable ratings. These cultural differences in positive self-evaluations are, thus, expected to contribute to differences in well-being. To test this hypothesis, the current study compared well-being across two countries, the United States and China. Participants from the two countries ( N = 271) reported on their life satisfaction and Big Five personality, which was used to indirectly measure their positive self-evaluation tendencies. We found cross-cultural differences with European Americans showing higher well-being and positively biased view of the self compared with Hong Kong Chinese. Importantly, cultural differences in positive evaluative bias mediated cross-cultural differences in well-being. The present study provides further support for the generalizability of cross-cultural differences in self-evaluation, and their influence on well-being.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.228
GPT teacher head0.531
Teacher spread0.302 · 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 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

Citations16
Published2015
Admission routes2
Has abstractyes

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