Overlap Between the General Factor of Personality and Trait Emotional Intelligence: A Genetic Correlation Study
Bibliographic record
Abstract
A previous meta-analysis (Van der Linden et al., Psychol Bull 143:36-52, 2017) showed that the General Factor of Personality (GFP) overlaps with ability as well as trait emotional intelligence (EI). The correlation between trait EI and the GFP was so high (ρ = 0.88) in that meta-analysis that these two may be considered virtually identical constructs. The present study builds on these findings by examining whether the strong phenotypic correlation between the GFP and trait EI has a genetic component. In a sample of monozygotic and dizygotic twins, the heritability estimates for the GFP and trait EI were 53 and 45%, respectively. Moreover, there was a strong genetic correlation of r = .90 between the GFP and trait EI. Additional analyses suggested that a substantial proportion of the genetic correlations reflects non-additive genetic effects (e.g., dominance and epistasis). These findings are discussed in light of evolutionary accounts of the GFP.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".