Bibliographic record
Abstract
This article briefly reviews traditional and modern tafsir (interpretation) trends and how modern Tafasīr have shaped contemporary Islamic thoughts. Classical Tafsir trends, Tafsir Bi al- ma’thur (tradition-based interpretation) and Tafsir Bi al-ra’y (reason-based interpretation) are well-documented in historical norms of Qur’ānic exegesis. However, modernity, with its complex socioeconomic, religious, political and cultural developments, presents unique challenges to interpreters (authors of Qur’ānic interpretations) to contextualize the Qur’ānic message and provide guidance to modern- day Muslims and their worldview. Complex modern Islamic thought is a selection of ideologies and philosophies that resulted from the prevailing diverse geopolitical, sociocultural and economic environment. These dynamic elements of modernity have conceptualized tafsir trends into the textualist, contextualize, modernist, socio-political, scientific, thematic and feminist approaches. These trends have not only transformed contemporary Islamic thought, and vice versa, but also continue to collectively evolve to meet the challenges of modernity. This article briefly reviews traditional and modern tafsir (interpretation) trends and how modern tafasir have shaped contemporary Islamic thought. Classical tafsir trends, tafsir bi al- ma’thur (tradition-based interpretation) and tafsir bi al-ra’y (reason-based interpretation) are well-documented in historical norms of Qur’ānic exegesis.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.943 | 0.959 |
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".