The ways of the world? Cross-sample replicability of personality trait-life outcome associations
Bibliographic record
Abstract
• Across three culturally diverse samples, lower order traits out-predicted multiple life outcomes than broad traits, such as domains. • Predictive accuracy of all levels was stronger in the English-speaking sample. • Narrow traits showed stronger predictive accuracy even when predicting outcomes from culturally different sample – suggesting a degree of universality to findings. • Individual trait associations were slightly more replicable for domains than narrower traits. Research in (mostly) Western samples has indicated that personality domains’ associations with life outcomes are replicable but often driven by their facets or nuances. Using three diverse samples (English-speaking, N=1,232; Russian-Speaking, N=1,604; Mandarin-speaking, N=1,216), we compared personality trait-outcome associations at domain, facet, and nuance levels, both within and among samples. Trait-outcome associations were at least moderately consistent among samples for all trait-hierarchy levels (average intraclass correlations = 0.64 to 0.74). Nuances provided the strongest predictive accuracy, both within and among samples. Trait-outcome associations were higher among English-speakers than Mandarin and Russian-speakers. Our observations suggested moderate generalizability among diverse samples, with nuances providing unique and replicable information. This offers potential to improve understanding of trait-outcome patterns.
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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 | 0.031 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".