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Record W2471987815 · doi:10.1002/ejsp.2214

Making sense of positive self‐evaluations in China: The role of sociocultural change

2016· article· en· W2471987815 on OpenAlexaff
Rui Zhang, Kimberly A. Noels, Yanjun Guan, Liping Weng

Bibliographic record

VenueEuropean Journal of Social Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSociocultural evolutionPsychologyOperationalizationSocial psychologyChinaSocioeconomic statusPriming (agriculture)TraitChinese cultureDevelopmental psychologySociologyDemographyPolitical scienceEpistemologyAnthropology

Abstract

fetched live from OpenAlex

Abstract Recent research points to Chinese people's elevated tendency to make positive self‐evaluations, despite the general claim that East Asians do not self‐enhance. We present three studies in support of a novel prediction that sociocultural change in China plays an important role in augmenting self‐enhancement. We operationalized self‐enhancement primarily in terms of the better‐than‐average effect (BTAE) and accounted for trait desirability or importance. We found that: (i) compared with Chinese Canadians, Chinese showed a stronger BTAE; (ii) within the Chinese, identification with contemporary Chinese culture uniquely predicted a stronger BTAE; and (iii) priming contemporary (vs. traditional) Chinese culture led to a stronger BTAE. Finally, we provided further evidence that motivation, in part, underlies the rising Chinese BTAE. We conclude by discussing the importance of both socioeconomic and cultural perspectives for understanding how and when of self‐enhancement in contemporary China and other societies undergoing social change.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score0.452

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 head0.151
GPT teacher head0.438
Teacher spread0.286 · 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 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

Citations21
Published2016
Admission routes1
Has abstractyes

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