Rereading al-Ṭabarī through al-Māturīdī: New Light on the Third Century Hijrī
Bibliographic record
Abstract
The Taʾwīlāt ahl al-sunna of al-Māturīdī, an exegete contemporaneous with al-Ṭabarī, is now available in three editions; we have no excuse for not consulting it. But the issue is not merely a matter of inspecting yet another of the Qur'an commentaries available. Rather, as will become apparent in this article, we have in the work of al-Māturīdī a fundamental early work that will revolutionise how we understand the development of the genre of tafsīr in medieval Islam. Recognising the central significance of the Taʾwīlāt will allow us to incorporate it as a major source alongside al-Ṭabarī, and will have profound implications for how we have been studying al-Ṭabarī and tafsīr as a whole. Tafsīr seen through the Taʾwīlāt al-Qurʾān looks different; it was pursued differently and speaks to a manner of doing tafsīr that al-Ṭabarī pretended did not exist. Al-Ṭabarī’s work must be read alongside al-Māturīdī’s: only then will we be able to fully grasp the significance of what al-Ṭabarī achieved. When read in this light, al-Ṭabarī is shown to be far more ideological, far more radical in his work, than we have hitherto realised. He was not gathering the Sunnī collective memory so much as reshaping it. Therefore, his work should be regarded as a representative of one particular type of tafsīr activity, rather than as the epitome of mainstream Sunnī Qur'an interpretation.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".