ShÄfiis theory of naskh and its influence on the UlmÌÆ° al-QurÄn
Bibliographic record
Abstract
The present thesis examines Shafi'i's theory of naskh and its influence on the 'ulum al-Qur'an. The thesis looks at two types of sources: internal and external. Internally, the discussion focuses on the origins of naskh, the background and construction of Shafi'i's theory of naskh, tracing the ingredients of Shafi'i's thought in general and of his theory of naskh in particular. Having established Shafi'i's theory of naskh, the thesis goes on to examine it externally by considering Shafi'i's influence on six authors of naskh books (Naḣḣas's al-Nasikh wa al-Mansukh, Makki's al-Idḍaḥ, Ibn al-Jawzi's Nawasikh al-Qur'an, Shu'lah's Ṣafwat al-Rasikh, Ibn al-'Ata'iqi's al-Nasikh wa al-Mansukh) and his impact on the exegesis of eight Qur'anic verses -Q. 2: 106, Q. 22: 52, Q. 45: 29, Q. 7: 154, Q. 16: 101, Q. 13: 39, Q. 4: 160, and Q. 3: 7, discussed by six authors of tafsir (Ṭabari's Jami' al-Bayan, Jaṣṣaṣ's Aḥkam al-Qur'an, Zamakhshari's al-Kashshaf , Ibn al-Jawzi's Zad al-Masir, Qurṭubi's al-Jami' li Aḥkam al-Qur'an, and Suyuṭi's al-Durr al-Manthur).
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".