Bibliographic record
Abstract
Ṣaḥīḥ Muslim, a well-known collection of Prophetic traditions, is considered to be a second most legitimate ḥadīth corpus after Ṣaḥīḥ al-Bukhārī. Number of translations and commentaries of this book have been written. Fatḥ al-Mulhim by Shabbīr Ahmad Usmānī and Minnat ul-Muʻim by Ṣafī-ur-Raḥmān Mubārakpūrī are two prominent Arabic commentaries written in Indo-Pak subcontinent. This article studies the methodologies of these two commentaries. It finds that along with number of commonalities, both the commentators have mentionable differences in elaborating the ḥadīths and developing the arguments in support of their opinions regarding the explanations of different ḥadīths and related words, phrases and personalities etc. Both offer arguments from Quran, ḥadīth, Arabic grammar and previous scholars etc. but Usmānī goes into more details of the relevant issues than Mubārakpūrī.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.969 | 0.983 |
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".