Interjudge Agreement, Self-Enhancement, and Liking: Cross-Cultural Divergences
Bibliographic record
Abstract
The authors investigated whether the lower self-enhancement found among Japanese is due to them being more accurate in their self-perceptions than Americans. Japanese and American participants were recruited from school clubs, where groups of five people rated each other and themselves. The Japanese sample was overall self-critical, whereas the American sample was overall self-enhancing. Moreover, as the desirability of the traits increased, Americans showed more self-enhancement, whereas Japanese showed more self-criticism. An accuracy account is unable to account for the cultural differences in self-enhancement because Americans showed more accuracy in their self-perceptions (as evidenced by self-peer agreement) than Japanese. Intracultural analyses further revealed that individual self-enhancement can be “unpackaged” by trait measures of independence and interdependence. Exploratory analyses of liking were also con ducted, revealing that American liking hinged on perceived similarity, self-verification, familiarity, and reflected-self-enhancement, whereas Japanese liking was based on familiarity, reflected self-enhancement, lower independence, and interdependence.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".