Similes in Parts Twenty-Ninth and Thirty of the Holy Quran
Bibliographic record
Abstract
This study aims to analyze the Quranic similes in Parts Twenty-ninth and Thirty of the Holy Quran as they contain 48 Surah from a total of 114 Surah, with a percentage of 42%. They are Makkan Surah except for four of them (Surah Al-Insan, Surah Al-Bayyina, Surah Al-Zalzala, Surah An-Nasr(. The study consists of two topics; the first one addressed the theoretical aspects of the term, the importance of simile and its role in clarifying the intended meaning. The second topic addressed the applied aspects by collecting, studying, analyzing, and examining the close linguistic meanings of similes, up to demonstrating their beauty and comparing them with other similes in other places in Quran. For this purpose, the researchers used the two tools of textual approach; description and analysis as the approach used in this study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".