Cultural differences in self- and other-evaluations and well-being: A study of European and Asian Canadians.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it