The Role of Positive Self-Evaluation on Cross-Cultural Differences in Well-Being
Bibliographic record
Abstract
Past studies have shown that North Americans have higher well-being compared with East Asians. Objective living conditions (e.g., wealth, education, personal and political freedom) have been found to substantially contribute to North Americans’ higher well-being. One other possible explanation is that North American culture fosters positive evaluations of the self to enhance self-esteem and to feel positive emotions, which may lead North Americans to provide favorable ratings. These cultural differences in positive self-evaluations are, thus, expected to contribute to differences in well-being. To test this hypothesis, the current study compared well-being across two countries, the United States and China. Participants from the two countries ( N = 271) reported on their life satisfaction and Big Five personality, which was used to indirectly measure their positive self-evaluation tendencies. We found cross-cultural differences with European Americans showing higher well-being and positively biased view of the self compared with Hong Kong Chinese. Importantly, cultural differences in positive evaluative bias mediated cross-cultural differences in well-being. The present study provides further support for the generalizability of cross-cultural differences in self-evaluation, and their influence on well-being.
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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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".