The Conscientiousness Paradox: Cultural Mindset Shapes Competence Perception
Bibliographic record
Abstract
Studies comparing personality across cultures have found inconsistencies between self–reports and measures of national character or behaviour, especially on evaluative traits such as Conscientiousness. We demonstrate that self–perceptions and other–perceptions of personality vary with cultural mindset, thereby accounting for some of this inconsistency. Three studies used multiple methods to examine perceptions of Conscientiousness and especially its facet Competence that most characterizes performance evaluations. In Study 1, Mainland Chinese reported lower levels of self–efficacy than did Canadians, with the country effect partially mediated by Canadian participants’ higher level of independent self–construal. In Study 2, language as a cultural prime induced similar effects on Hong Kong bilinguals, who rated themselves as more competent and conscientious when responding in English than in Chinese. Study 3 demonstrated these same effects on ratings of both self–perceived and observer–perceived competence and conscientiousness, with participants changing both their competence–communicating behaviours and self–evaluations in response to the cultural primes of spoken language and ethnicity of an interviewer. These results converge to show that self–perceptions and self–presentations change to fit the social contexts shaped by language and culture. Copyright © 2013 European Association of Personality Psychology
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".