Bibliographic record
Abstract
Qazi Sanaullah Pani Patti was one of the eminent scholars of subcontinent. He belonged to the Mujaddidi order of Sufism, which is the main branch of Naqshbandi Sufi tariqah. He was the author of many books. One of the major contribution by Qazi Sanaullah is in the field of Tafseer. He wrote a famous tafseer on the name of one of his teachers Mazhar Jane Janan. Name of the tafseer is “Tafseer e Mazhari”. In the given research article I have discussed the ways Qazi Sanaullah prefers opinions of some scholars over opinions of other scholars in his tafseer. As rules of preferences is a great concern for those interpreting the words of Quran because this requires careful consideration and thought in the context of different opinions, and linking them to reach a conclusion, similarly it necessitates for the exegetes that these rules be in the light of Quran and Sunnah and not their personal opinions. This research paper discusses the ways of preference used by Qazi Sanaullah.
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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) | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.984 | 0.984 |
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".