Which studies test whether self‐enhancement is pancultural? Reply to Sedikides, Gaertner, and Vevea, 2007
Bibliographic record
Abstract
What types of studies test the question of pancultural self‐enhancement? Sedikides, Gaertner, and Vevea (2007) have identified inclusion criteria that largely limit the question to studies of the better‐than‐average effect (i.e. 27 out of 29 effects that they include as ‘validated’ and ‘relevant’). In contrast, other effects which they labelled as ‘unvalidated’ or ‘irrelevant’ used methods other than the better‐than‐average effect (i.e. 24 out of 24 effects). Because Sedikides et al . are drawing conclusions about pancultural self‐enhancement and not the pancultural better‐than‐average effect, these excluded studies are relevant to the hypothesis under question. Ignoring the findings from other methods is highly problematic, in particular because these other methods yield results that conflict with those from the better‐than‐average effect. An analysis of effects from all studies reveals no support for pancultural self‐enhancement.
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How this classification was reachedexpand
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) | 0.001 | 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) | 0.002 | 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 teacher head, 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".