Toward an explanation of cultural differences in subjective well-being: the role of positive emotion norms and positive illusions
Bibliographic record
Abstract
The present research explores the role of positive emotion norms and positive illusions in explaining the higher subjective well-being observed among Europeans compared to East Asians in Canada. Specifically, we investigate the underlying psychological mechanisms contributing to the prevalence of positive self-views among individuals with European backgrounds, characterized by individualism, versus those with East Asian backgrounds, associated with collectivism. Our study compares Europeans and East Asians in Canada to determine whether cultural norms regarding positive emotions account for the elevated positive self-views and subjective well-being in Europeans. With a sample of 225 participants (112 Europeans and 113 East Asians), our findings reveal significant indirect effects of culture on subjective well-being through positive emotion norms and positive illusions. This study highlights that Europeans, compared to East Asians, believe it is more appropriate to experience and express positive emotions, and this norm influences their positive self-views, subsequently impacting subjective well-being. These findings offer valuable insights into how cultural factors shape subjective well-being across different groups.
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 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 | 0.005 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 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".