Revisiting Modern<i>Naẓm</i>Approaches to the Qur'an: Iṣlāḥī’s Interpretation of Q. 107 and Q. 108 in his<i>Tadabbur-i Qurʾān</i>
Bibliographic record
Abstract
Modern naẓm approaches to the Qur'an ask for a detailed study of the interpretive methodologies and assumptions that function behind them. In order to understand such structural approaches, the present article offers a focused study of two important suras of the Qur'an (Q. 107 and Q. 108), involving some polysemous words, in the Urdu tafsīr of Amīn Aḥsan Iṣlāḥī (d. 1997), Tadabbur-i Qur’ān. Iṣlāḥī’s theory of naẓm has gained considerable attention in the academy and is worthy of investigating from new perspectives. It is built on a holistic and unified system of connections within a sura and between suras. The paper aims to investigate the mechanism through which Iṣlāḥī identifies naẓm, and examine its relationship with specific meaning and historical context of a sura. It argues that Iṣlāḥī’s concept of naẓm, which he presents as an internal feature of the Qur'an, seems to be based on and shaped by a specific view of the life of Prophet Muḥammad. It appears that non-linguistic factors play a pivotal role in his theory of naẓm. Therefore, in order to fully comprehend his system of linkages in the Qur'anic discourses, there is a need to further investigate how he understands the biography of the Prophet and the early Islamic history in comparison to other exegetes and historians, and how he approaches the question of the authenticity of our knowledge about the details of Muhammad.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".