How many emotional intelligence abilities are there? An examination of four measures of emotional intelligence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The ability model of emotional intelligence (EI) specifies that four related abilities are involved: perceiving emotions, facilitating thought using emotions, understanding emotions, and managing them. Several performance-based assessments have been developed to measure those four abilities. Although some researchers find empirical support for the four abilities, others have argued that emotional intelligence divides into three abilities, two or even a single, unitary ability (Legree et al., 2014; Palmer, Gignac, Manocha, & Stough, 2005). We reanalyzed archival data from four ability tests of emotional intelligence, Ns = 503, 5000, 1000, and 2000, conducting item-level exploratory factor models of all four assessments for the first time. Based on those analyses, we suggest possible revisions of the 4-factor model to guide future research and assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it