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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".