Bibliographic record
Abstract
Sheikh Nūr al-Ḥasan al-Uwaisī (1268AH-1348AH) was a great religious scholar of the subcontinent and an icon of spiritualism. He was also an expert of Qur’ānic sciences. In this area of knowledge he wrote an exegesis titled “Nūr al-Wāʻiẓīn". Unfortunately Sheikh al-Uwaisī’s achievements particularly his referred work was unknown to scholarly world. “Nūr al-Wāʻiẓīn" was written almost a century ago, but is not yet available in print. It is the need of the time to introduce this great scholar and bring out his magnum opus “Nūr al-Wāʻiẓīn" before the scholarly world. So, the present article centers a brief exploratory study of Sheikh al-Uwaisī and his achievements particularly his referred tafsīr manuscript. It maintains that “Nūr al-Wāʻiẓīn" consists of various unique characteristics and therefore is a blessing for students and scholars that they must take advantage of.
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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.972 | 0.980 |
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".