Which Cultural Dimensions Predict Variations in Emotional Conformity? An Extension of Vishkin et al. (2023) Across 28 Nations
Bibliographic record
Abstract
Despite being a classic social psychology topic, cultural variability in conformity has only been examined systematically in the last few decades. Vishkin et al. reported evidence that conformity of experienced emotions and of valued emotions is stronger in individualistic cultures. We tested the replicability of this finding using data from 28 nations ( N = 6,168), incorporating two further relevant cultural predictors of cultural differences: flexibility-monumentalism and tightness-looseness. Contrasting effects regarding valence were found for conformity of experienced emotions and of valued emotions. Conformity of experienced positive emotions and of valued negative emotions was predicted by individualism, monumentalism, and looseness. The results are discussed in terms of the distinction between injunctive and descriptive norms and cultural variations in the salience of positive and negative emotions. Using additional indicators of cultural difference yields a fuller understanding of these effects than that provided by the contrast between individualism and collectivism. The use of deviation scores provides a useful operationalization of variations in conformity.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".