Diversity from within: The Impact of Cultural Variables on Emotion Expressivity in Singapore
Bibliographic record
Abstract
Culture is intrinsically linked with emotion expression, as culture provides rules regarding how to manage emotions when they occur. Thus far, existing literature has extensively compared norms for emotional expression and suppression, revealing significant differences among culturally distinct but also geographically distant groups (e.g., “collectivistic” Chinese versus “individualistic” U.S. Americans). The present study examines the impact of cultural diversity within Singapore, a heterogeneous Asian nation of 5.4 million residents. Using an expression suppression paradigm, eighty-three participants viewed emotion eliciting video clips and their expressions were analyzed according to the Emotion Facial Action Coding System (EmFACS, Ekman, Irwin, & Rosenberg, 1994) for signs of happiness and disgust. Participants tasked to manage their expression were successful; however cultural indicators such as ethnicity, collectivism, and concern for face affected expressivity under both suppression and natural expression conditions. These results emphasize the importance of exploring culture within national boundaries, as multiple cultural factors (e.g., ethnic groupings, values, and face) influenced expression.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".