Bibliographic record
Abstract
In recent times, there has been much discourse on the significance and function of the term jurisprudence of minorities (fiqh al-aqalliyya). The term, which is also called diasporic jurisprudence, refers to the issuance of juristic ordinances to accommodate the needs of Muslims residing in non-Muslim majorities, with special requirements that may not be appropriate for other communities. This paper argues that, due to the hierarchical nature of leadership inherent in Shi‘ism, Shi'i jurists (maraji)‘ have responded to the needs of their communities that live as minorities in the West by recasting Islamic legal discourse on Muslim minorities and reconciling Islamic legal categories to the demands of the times. It will demonstrate this genre of jurisprudence addresses a wide range of topics that were either not traditionally discussed in Islamic juridical manuals or represent a revision of earlier formulations. The paper demonstrates that most of the rulings are casuistic in nature and do not represent a fully-fledged legal system. Many edicts have been either imported to the diaspora or relaxed when abiding by these injunctions have created difficulties (haraj) for the faithful believers. As will be discussed, Shi‘i minority fiqh is restricted to the collection of fatawa (religious edicts) produced in the seminaries by jurists who do not fully comprehend the challenges experienced by their followers living in the diasporic milieu.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".