Bibliographic record
Abstract
The Holy Quran is a message of civilization, a book of religion, and a way of life. A nation can only progress and live in harmony with other civilizations and nations by adhering to it. Otherwise, ignorance and backwardness will accompany it, along with the loss of its identity, the extinction of its civilization, and the loss of its religion. The question that arises is the best methodology for dealing with the Quran. The methodology outlined by the Quran for dealing with it is the methodology of contemplating its verses. Our esteemed interpreters from the early generations laid down various methodologies for dealing with the Quran through their contemplation, in light of the challenges of their times and regions. This enables us to benefit from the teachings and guidance of the Quran in the modern era. Contemplation of the Quran is an ongoing process, adapting to the needs of each era and society, to connect our nation to the strong bond of Allah and derive solutions for contemporary issues and face current challenges. Therefore, the focus of this study is on some of the prominent contemporary interpreters through their books in Quranic studies and contemplation. These figures are: Abdul Rahman Habnaka al-Midani (died: 1978), Abu al-Hasan Ali al-Nadwi (died: 1999), and Taha Jaber al-Alwani (died: 2016). The study aims to examine the proposed methodologies of contemplation among them and extract the key features.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.963 | 0.973 |
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".