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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.994 | 0.980 |
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; both teacher heads 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".