A network analysis of relationships between the Trait Emotional Intelligence Questionnaire Short Form (TEIQue-SF) and the Big Five personality traits in Italian workers
Bibliographic record
Abstract
This study investigated the relationship between the Trait Emotional Intelligence Questionnaire Short Form (TEIQue-SF) and the ten facets of the Big Five Questionnaire (BFQ) via network analysis. The TEIQue-SF and the BFQ were administered to 751 Italian workers. Both centrality indexes (Expected Influence and Node Predictability) and bridge dimensions were calculated. Stability and accuracy were also checked to ensure the reliability of the findings. The BFQ facets of Perseverance (Consciousness) had the highest centrality while Emotion control (Emotion stability, the opposite of Neuroticism) showed high centrality. Among TEIQue-SF dimensions, Sociability followed by Emotionality and Self-Control had high centrality. TEIQue-SF Emotionality and Sociability had a bridge function. Both TEIQue-SF Emotionality and Sociability and both BFQ Perseverance and Emotion control are relevant in linking emotional intelligence and personality traits. Although further studies are needed, the network analysis represents a promising approach in providing a more detailed analysis of the relationships between dimensions of emotional intelligence and facets of personality traits in workers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".