A behavioral genetic study of trait emotional intelligence.
Bibliographic record
Abstract
Numerous models of emotional intelligence (EI) have proposed the existence of hitherto undiscovered mental abilities, competencies, and skills. The theory of trait emotional intelligence suggests that the content domains of these models invariably contain permutations of personality traits. The two studies in this article examine the heritability of trait EI scores with a view to demonstrating empirically that the construct has a similar level of genetic influence as other personality traits. Study 1 was a family design of 133 high-school students and their parents. Regressions of offspring on midparent scores suggested median upper-limit heritability estimates of .18 at facet level, .25 at factor level, and .32 at the global trait EI level. Study 2 was a twin design (213 pairs of monozygotic [MZ] twins and 103 pairs of dizygotic [DZ] twins). It yielded median heritabilities of .42 for the facets, .44 for the factors, and .42 for global trait EI. Overall, our findings are in accordance with studies of the major personality dimensions and provide further empirical support for the conceptualization of EI as a personality trait.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".