An extension of the dark triad and five‐factor model to three Asian societies
Bibliographic record
Abstract
The aim of this study is to test the external validity of the short dark triad (SD3) and see whether the dark triad (DT) framework is replicable in Asian societies. In three independent studies involving 443 participants from South Korea, China, and the Philippines, we extend short measures of the DT and the big five framework as well as conduct confirmatory factor analysis to test various models of the SD3 in each country. We also test for measurement invariance, report intercorrelations, alpha coefficients, and gender differences within each sample. Except for Machiavellianism, all models failed configural invariance. Despite failing configural invariance across all three countries, males consistently reported higher means on all DT traits, with psychopathy the lowest, and DT constructs were significantly intercorrelated with each other across all three samples, demonstrating a pattern of consistency with previous DT literature and findings. As the DT continues to gain in popularity and is beginning to surface in cross‐cultural research, we contribute to discussions concerned with the construct's external validity. Our study's findings question what, if any, meaningful interpretations can be made from extending Western‐based personality instruments to Asian (foreign) societies. Limitations and contributions of this study are outlined, and recommendations for future research are summarized.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".