Bibliographic record
Abstract
In the foregoing chapter, I took up two out of four developments that contributed to the formation of the Sharīʿa, namely, the judiciary and the legal schools. Part II of this book will present a conspectus of fiqh , the third development. In this chapter, the aim is to sketch the fourth and last component of the Sharīʿa, namely, legal theory, properly known as uṣūl al-fiqh . In the previous chapter, I also discussed the Great Synthesis, which gave rise to a foundational definition of the conflated roles of reason and revelation in Sunnite Islam. Legal theory was perhaps the most determinative manifestation of this Synthesis which, in its final stages, emerged around the middle of the fourth/tenth century. This, needless to say, is precisely the period that witnessed the elaboration of the first complete system of legal theory. It is not easy, however, to reconstruct this system from the fragmentary sources that have survived from that period. Thus, to offer an informative and – for the later period – representative account of this theory, I utilize mainly the prolific and magnificently elaborated sources from the fifth/eleventh century, but not without occasional references to earlier and later works. The choice of that century has to recommend it the added fact that its theoreticians produced some of the most influential treatises for the course of theoretical developments in the centuries to come.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".