Bibliographic record
Abstract
“Al-Fawz al-Kabīr fī Usūl al-Tafsīr” by Shāh Walīullāh (1703-1762) has been venerated as a great achievement in the field of the principles of Qur’ānic exegesis. In this book, he has divided the Qur’ānic subjects into five types: ʻIlm al-Mukhāṣamah, ʻIlm al-Aākām, ʻIlm al-Tazkīr bi Aʻlāillāh, ʻIlm al-Tazkīr bi Ayyāmillāh and Ilm al-Tazkīr bil Mawt. These subjects have been used in various ways. Their effects can also be seen in commentaries of the Quran. If the latter three subjects are given the title of "Tazākīr-e-Thalāthah", they become three main subjects instead of five. This article aims to study the effects of "Tazākīr-e-Thalāthah" on exegetical literature, and in this regard, the focus has been on “Tadabbur-e-Quran” and “Tibyān al-Quran”. A closer look at the topics and discussions related to "Tazākīr-e-Thalāthah" in both the commentaries makes it clear that the effects of this division and understanding of "Tazākīr-e-Thalāthah" on both the commentaries are obvious in some matters and in some issues they have adopted their own method or traditional style of exegesis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.981 | 0.984 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".