Fear Goliath or David? Inferring Competence From Demeanor Across Cultures
Bibliographic record
Abstract
We examined cultural differences in people's lay theories of demeanor-how demeanor may be perceived as a straightforward and reliable reflection of reality (convergence theory) or as a deviating reflection of reality (divergence theory). Across different domains of competition, Euro-Canadians perceived greater competence in an opponent with a competent demeanor, whereas Chinese paradoxically perceived greater competence in an opponent with no signs of competence (Studies 1-4b). The results, unexplained by attributional styles (Study 1), likability (Study 3), or modesty (Study 3), suggest that Euro-Canadians endorse a stronger convergence theory than Chinese in their inferences of competence. Corroborated with qualitative data (Study 4a), such cultural differences were explained by the beliefs that demeanor can be a misleading reflection of reality, verified in college and community (Study 4b) samples. We discuss the implications for social perception, intergroup dynamics, and self-presentation in competitions.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".