Bibliographic record
Abstract
Syed Amīr ̒Alī Maīh Ābādī is one of the leading scholars of Tafsīr of the Quran in the subcontinent. His Tafsīr known as “Mawāḥib al-Raḥmān” is a big achievement in the relevant field. The author of this article has conducted research about referred Tafsīr from two aspects, in brief, to obtain the higher study degree, the author has carried out his M. Phil degree thesis from International Islamic University Islamabad and covered its one aspect “Manhaj al-Tafsīr” and then to cover its jurisprudential aspects. The author presented his dissertation of PhD in Mohi ud Din Islamic University. Due to the substantial working on this particular subject, he had the opportunity to study this Tafsīr and it enhanced the eagerness of the author of this article. During the study of this Tafsīr, the author came across distinctive aspects of this Tafsīr. One of them is, though all the writers of Tafsīr have already written down on all the related subjects which are the supportive in Tafsīr of the verses of the Quran and either the foremost but apart form that Syed Amīr ̒Alī kept the focus on the some particular subjects of the Quran. He explained this thing in the preface of the Tafsīr that some important aspects of the Quran were mainly focused and he also explicated the rationale and his own interest for it and he unequivocally explained that he considered the fourteen aspects while doing Tafsīr. An attempt is made in this article to highlight the fourteen points and the aspects of the author of this Tafsīr that he particularly focused on these aspects and to explain the importance of those points.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.959 | 0.966 |
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".