Cross-Situational Self-Consistency in Nine Cultures: The Importance of Separating Influences of Social Norms and Distinctive Dispositions
Bibliographic record
Abstract
We assessed self-consistency (expressing similar traits in different situations) by having undergraduates in the United States ( n = 230), Australia ( n = 220), Canada ( n = 240), Ecuador ( n = 101), Mexico ( n = 209), Venezuela ( n = 209), Japan ( n = 178), Malaysia ( n = 254), and the Philippines ( n = 241) report the traits they expressed in four different social situations. Self-consistency was positively associated with age, well-being, living in Latin America, and not living in Japan; however, each of these variables showed a unique pattern of associations with various psychologically distinct sources of raw self-consistency, including cross-situationally consistent social norms and injunctions. For example, low consistency between injunctive norms and trait expressions fully explained the low self-consistency in Japan. In accord with trait theory, after removing normative and injunctive sources of consistency, there remained robust distinctive noninjunctive self-consistency (reflecting individuating personality dispositions) in every country, including Japan. The results highlight how clarifying the determinants and implications of self-consistency requires differentiating its distinctive, injunctive, and noninjunctive components.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".