Bibliographic record
Abstract
The hadith is divided into two parts.one is called Matan e Ḥadīth and the other is called Sanad e Ḥadīth (the chain of transmission).The chain of transmission is the most important source of ḥadīth. It is not possible to preserve a ḥadīth without a sanad. Based on this, the criteria for examining and accepting the ḥadīth have been established. In view of the importance of the sanad, the narrators further developed it, as a result of which the knowledge of Rijāl (‘Ilm e Rijāl) came into existence. Muḥaddisīn have done research on this magnumopus art from various angles. Permanent works have also been written in this regard. The commentators of ḥadīth have also discussed it in detail in the commentaries of the books of ḥadīth. The name of Moulāna Khalīl Aḥmad Sahāranpurī appears prominently in the commentators of the ḥadīth who have presented a critical study of the situation of the narrators. If it is said that Bazl-al- Majhūd is an encyclopedia of Sunan Abū Dā’wūd’s rijāl, it is correct. Hence, Moulāna Khalīl Aḥmad Sahāranpurī has talked about the tradition in sanad, in this regard, he has also pointed out the errors of the previous commentators of the ḥadīth with rhetoric arguments. During the discussion on ruwāt, he judiciously adopted all the methods adopted by the experts while discussing the narrators.
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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 | 0.002 |
| 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 | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.966 | 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".