Inclusion of additional studies yields different conclusions: Comment on Sedikides, Gaertner, & Vevea (2005), <i>Journal of Personality and Social Psychology</i>
Bibliographic record
Abstract
In a Journal of Personality and Social Psychology article, Sedikides, Gaertner and Vevea (2005) presented two meta‐analyses that included eight papers to investigate the question of whether people from Eastern cultures self‐enhance more for traits that they view to be important compared to those that they view as unimportant. The results supported their hypothesis: Self‐enhancement appears to be pancultural. However, this conclusion is severely compromised by six relevant papers that are not included in their meta‐analyses. Importantly, all of these six studies contradicted their hypothesis. When complete meta‐analyses are conducted which include all of the relevant papers, a very different pattern of results emerges. Eastern and Western cultures do not differ from each other in the pattern of their self‐enhancement of independent and interdependent traits. Furthermore, whereas Westerners self‐enhanced significantly more for traits that they viewed to be especially important, East Asians did not. Contrary to the Sedikides et al. (2005) suggestion, the existing evidence suggests substantial cross‐cultural variation in self‐enhancement, with Westerners being far more self‐enhancing than Easterners. Reasons for the conflicting pattern of findings across methods and meta‐analyses 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 | 0.155 | 0.442 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.014 | 0.004 |
| Research integrity | 0.026 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".