Tafsir in its Context, or How Socio-Political Milieu Informed What to Include and Exclude in Qur’an Commentaries: At-Tabari’s, Ibn Abi Hatim’s, and al-Maturidi’s Interpretations of Surah Al-Ma’idah (5): 51 as a Case Study
Bibliographic record
Abstract
This paper analyzes the interpretations of Qur’an surah al-Ma’idah (5): 51 by three 10th-century exegetes: at-Tabariy, Ibn Abi Hatim, and al-Maturidiy. It argues that at-Tabariy’s commentary, though foundational, represents just one model of exegesis in the medieval period. Ibn Abi Hatim and al-Maturidiy incorporate unique interpretative materials, not found in at-Tabariy’s work. Al-Maturidiy offers three types of interpretation, while Ibn Abi Hatim narrates a story about Caliph ‘Umar’s anger at a companion hiring a Christian secretary. The study also contextualizes these interpretations within the socio-political environment of the 10th-century ‘Abbasid era, where non-Muslims gained influence. This article concludes that the Qur’an commentators’ decision to include and exclude certain interpretative materials in their tafsir works reflects, to some degree, the socio-political environments in which they lived and authored their works. Qur’an commentaries, like any other book, were not written in a vacuum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".