The Nature and Measurement of Emotional Intelligence Abilities: Basic Dimensions and Their Relationships with Other Cognitive Ability and Personality Variables
Bibliographic record
Abstract
Dimensions of Emotional Intelligence (EI) were derived, and their place with respect to the cognitive ability and personality domains was examined. A factor analysis of 24 maximum-performance and self-report EI measures administered to an undergraduate sample ( N= 176) yielded five factors: Emotional Congruence, Emotional Independence, Social Perceptiveness, Alexithymia, and Social Confidence. Emotional Congruence had lowcorrelations with four cognitive ability factors and Big Five personality factors, indicating that it may represent either a new psychological construct or a method factor. Social Perceptiveness correlated significantly with cognitive abilities, indicating its place in this domain. The remaining three factors had moderate correlations with various personality dimensions and low correlations with cognitive abilities, indicating that they fall outside the latter domain. On the basis of the present results, only maximum-performance and not self-report measures of EI can be seen as tapping the cognitive ability domain.
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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 | 0.007 |
| 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 | 0.001 | 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 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".