Bibliographic record
Abstract
The present paper studies Ziāur Rahmān al-ʻAzmī’s conversion to Islam, preaching of Islam and works related to Quran. It traces that al-ʻAzmī impressed by Mawdūdī's book "Dīn-e-Ḥaq" and his spirit for martyrdom in the Movement for the finality of the Prophethood of Prophet Muhammad (ﷺ). He also impressed by one of his teacher's teaching of the Quran. He later converted to Islam. After converting to Islam, he faced countless difficulties, but his stability did not falter. He studied Arabic and Islamic studies at the famous religious school, Dār al-Salām. Later he continued his religious education in Medina and Mecca. He received his PhD from Azhar University and became a professor in the Department of Ḥadīth at Medina University. He also became the head of the same department and held various administrative positions at Medina University. He also had the privilege of teaching Saḥīḥ al-Bukhārī and Saḥīḥ Muslim in the Prophet's Mosque. He wrote dozens of books in Urdu, Arabic and Hindi on various topics. He wrote two prominent books on Quran titled: "Quran kī Shatal Chāūn" and "Qurānī Encyclopedia" in Hindi, which have been translated into Urdu and English. He preached the message of Islam to the people of different religions in various books such as “Ganga say Zamzam tak", "Al-Darāsāt fī al-Yahūdiyyah wa al-Nasrāniyyah", "Fusūl al-Ḥind". His Daʻwah and scholarly writing activities had had a profound effect. Through these activities, many people entered the realm of Islam. His scholarly services continue to be used and this use will continue forever.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.960 | 0.966 |
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".