Aspects of Emotional Intelligence Based on the Noble Qurʾān: An Analytical Study
Bibliographic record
Abstract
One of the most important fields of research is the effort to develop human abilities in various areas and to use them for the advancement of life and the improvement of one's livelihood. The Qurʾān, which Muslims believe that was the last book revealed by Almighty Allāh (God), gives many examples of how emotions help people and make them happier. The Qurʾān is a book that deals with emotions by using its guidance and helps refine people's emotions which were stated its various Sūrahs (chapters). This research makes an attempt to understand emotional Intelligence (EI), and to determine its aspects and skills based on Qurʾānic wisdoms. To investigate this issue, this study uses a qualitative data using both deductive and inductive approaches. The findings presented in this article are the outcomes of a thematic analysis of Qur'ānic verses (texts) related to the EI. This research identifies five aspects of EI: Religious, Psychological, Social, Environmental, and Divine Laws. It also identifies forty basic skills derived from Qurʾānic verses. Finally, it suggests further studies to come up with more EI skills that encompass all human aspects and achieve its goals and meet its needs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".