Within- and between-group heterogeneity in cultural models of emotion among people of European, Asian, and Latino heritage in the United States.
Bibliographic record
Abstract
= 1,618; 490 of European heritage, 463 of Asian heritage, 665 of Latino heritage) provided data on the desirability and appropriateness of experiencing 19 specific emotions in daily life, as well as their U.S. cultural orientation and sociodemographic characteristics. Four distinct classes/models of emotion desirability and four classes/models of emotion appropriateness emerged. Latent class regression demonstrated that endorsement of emotion models was systematically related to heritage group membership and mainstream cultural orientation. Findings suggest meaningful within-group heterogeneity in emotion models and highlight the ways in which emotion models among people of Latino heritage are both similar to and distinct from models among people of European and Asian heritage. By developing a more nuanced understanding of between- and within-group variation in emotion models and highlighting the Latin American form of collectivism as in need of further research, this study advances cultural psychology, affective science, and their integration. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".