Emotional Intelligence Among School Teachers in Oman
Bibliographic record
Abstract
This paper sheds light into the role of emotional intelligence in school teaching. It reports the findings of a study that sought to investigate the emotional intelligence of 4,098 school teachers in the Sultanate of Oman in five dimensions using the Schutte Emotional Intelligence Scale (SEIS). The study also measured the relationship between teachers’ emotional intelligence and a number of demographic teacher and school variables. The results showed that teachers’ emotional intelligence was high in all five dimensions. There were variations in teachers’ emotional intelligence based on certain teacher and school variables. Key words: job satisfaction, Sultanate of Oman, teachers’ emotional intelligence. Cet article fait la lumière sur le rôle de l’intelligence émotionnelle dans l’enseignement. L’article fait rapport des résultats d’une étude ayant porté sur l’intelligence émotionnelle, selon cinq dimensions, de 4 098 enseignants dans le Sultanat d’Oman en employant l’échelle de Schutte sur l’intelligence émotionnelle. L’étude a également mesuré le rapport entre l’intelligence émotionnelle des enseignants et un certain nombre de variables démographiques liées aux enseignants et aux écoles. Les résultats indiquent que l’intelligence émotionnelle des enseignants étaient élevée pour toutes les dimensions. Des variations dans l’intelligence émotionnelle des enseignants se sont révélées selon les variables liées aux enseignants et aux écoles. Mots clés : satisfaction professionnelle; Sultanat d’Oman; intelligence émotionnelle chez les enseignants
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".