Cultural differences in self- and other-evaluations and well-being: A study of European and Asian Canadians.
Bibliographic record
Abstract
Anusic, Schimmack, Pinkus, and Lockwood (2009) developed the halo-alpha-beta (HAB) model to separate halo variance from variance due to valid personality traits and other sources of measurement error in self-ratings of personality. The authors used a twin-HAB model of self-ratings and ratings of a partner (friend or dating partner) to test several hypotheses about culture, evaluative biases in self- and other-perceptions, and well-being. Participants were friends or dating partners who reported on their own and their partner's personality and well-being (N = 906 students). European Canadians had higher general evaluative biases (GEB) than Asian Canadians. There were no cultural differences in self-enhancement or other-enhancement. GEB significantly predicted self-ratings of life satisfaction, but not informant ratings of well-being. GEB fully mediated the effect of culture on self-ratings of life satisfaction. The results suggest that North American culture encourages positive biases in self- and other-perceptions. These biases also influence self-ratings of life satisfaction but have a much weaker effect on informant ratings of life satisfaction. The implications of these findings for cultural differences in well-being are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".