A Discourse Analysis of Surah Al-Duha in the Holy Quran: Implications for Contemporary Discourse
Bibliographic record
Abstract
This study employs a discourse analysis (DA) framework to explore the linguistic and textual features of Surah Al-Duha in the Holy Quran and its implications for contemporary discourse. The study aims to gain a deeper understanding of the discursive elements and patterns of the Surah and its context to explain how language is used to shape meaning and construct social structures and ideologies. The study used a qualitative research design that draws on the principles of DA to analyze the Surah's grammar, syntax, lexicon, and metaphorical language. The study also explained the historical, social, and cultural context of the Surah to explore its relevance and implications for contemporary discourse. The findings of the study showed that several discursive strategies and patterns were employed in the text, including the use of metaphorical language, intertextual references, and repetition of certain words and phrases. These strategies and patterns contribute to the overall meaning and interpretation of the text and highlight how power, identity, and ideology are constructed and negotiated in religious discourse. The study presents an original and valuable contribution to the field of discourse analysis and the understanding of the Quran as a religious text.
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 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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".