MétaCan
Menu
Back to cohort
Record W4375858325 · doi:10.5539/ass.v19n3p1

Aspects of Emotional Intelligence Based on the Noble Qurʾān: An Analytical Study

2023· article· en· W4375858325 on OpenAlexvenueno aff
Yasser Mohamed Tarshany, Fouad Bounama

Bibliographic record

VenueAsian Social Science · 2023
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsEmotional intelligencePsychologyThematic analysisLivelihoodSocial psychologyQualitative researchEpistemologySociologySocial science

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.111
GPT teacher head0.423
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAsian Social ScienceSame topicEmotional Intelligence and PerformanceFrench-language works237,207