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Record W2332529100 · doi:10.1037/a0029118

Not all collectivisms are equal: Opposing preferences for ideal affect between East Asians and Mexicans.

2012· article· en· W2332529100 on OpenAlexaff
Matthew B. Ruby, Carl F. Falk, Steven J. Heine, Covadonga Villa, Orly Silberstein

Bibliographic record

VenueEmotion · 2012
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCollectivismAffect (linguistics)PsychologySocial psychologyEast AsiaHarmony (color)IndividualismEnthusiasmGeographyPolitical scienceArtChina

Abstract

fetched live from OpenAlex

Previous research has revealed differences in how people value and pursue positive affect in individualistic and collectivistic cultural contexts. Whereas Euro-Americans place greater value on high activation positive affect (HAP; e.g., excitement, enthusiasm, elation) than do Asian Americans and Hong Kong Chinese, the opposite is true for low activation positive affect (LAP; e.g., calmness, serenity, tranquility). Although the form of collectivism present in East Asia dictates that individuals control and subdue their emotional expressions so as to maintain harmonious relationships, the opposite norm emerges in Mexico and other Latin American countries, in that the cultural script of simpatía promotes harmony through the open and vibrant expression of positive emotion. Across two studies, we found that Mexicans display a pattern of HAP/LAP preference different from those from East Asian collectivistic cultures, endorsing HAP over LAP.

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)0.001
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.249
GPT teacher head0.400
Teacher spread0.151 · 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

Citations145
Published2012
Admission routes1
Has abstractyes

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