Heterogeneity of long-history migration explains cultural differences in reports of emotional expressivity and the functions of smiles
Bibliographic record
Abstract
A small number of facial expressions may be universal in that they are produced by the same basic affective states and recognized as such throughout the world. However, other aspects of emotionally expressive behavior vary widely across culture. Just why do they vary? We propose that some cultural differences in expressive behavior are determined by historical heterogeneity, or the extent to which a country's present-day population descended from migration from numerous vs. few source countries over a period of 500 y. Our reanalysis of data on cultural rules for displaying emotion from 32 countries [n = 5,340; Matsumoto D, Yoo S, Fontaine J (2008) J Cross Cult Psychol 39(1):55-74] reveals that historical heterogeneity explains substantial, unique variance in the degree to which individuals believe that emotions should be openly expressed. We also report an original study of the underlying states that people believe are signified by a smile. Cluster analysis applied to data from nine countries (n = 726), including Canada, France, Germany, India, Indonesia, Israel, Japan, New Zealand, and the United States, reveals that countries group into "cultures of smiling" determined by historical heterogeneity. Factor analysis shows that smiles sort into three social-functional subtypes: pleasure, affiliative, and dominance. The relative importance of these smile subtypes varies as a function of historical heterogeneity. These findings thus highlight the power of social-historical factors to explain cross-cultural variation in emotional expression and smile behavior.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".