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Record W2977588377 · doi:10.1017/9781108766579.006

The Challenges of Islamic Law Adjudication in Public Reason

2020· book-chapter· en· W2977588377 on OpenAlexaff
Mohammad Fadel

Bibliographic record

VenueCambridge University Press eBooks · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicAmerican Constitutional Law and Politics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAdjudicationIslamPublic reasonShariaLawDeferencePolitical scienceIdeal (ethics)Public lawIdeal theoryLaw and economicsSociologyPhilosophyPoliticsDemocracyMathematics

Abstract

fetched live from OpenAlex

John Rawls’s conception of public reason precludes the enforcement of rules derived from metaphysically controversial doctrines, which seems to exclude adoption of Islamic legal doctrines as legitimate rules of decision. While that is true as a matter of ideal theory, the relationship of public reason to Islamic law in nonideal theory is more complex. Islamic law is directly incorporated in the legal systems of numerous Muslim and non-Muslim jurisdictions throughout the world, or its rules arise incidentally in various cases where Islamic law is not formally part of the legal order. This chapter argues that the idea of public reason can meaningfully guide public reason–minded judges when they are tasked with applying Islamic law in a fashion that vindicates the ideals of public reason. Public reason requires judges to steer a middle course among possible extremes when an issue of Islamic law arises: theological reasoning, extreme deference to historical norms, or principled abstention. Moreover, by adhering to the idea of public reason in these cases, judges can play in important role in strengthening, or bringing about, an overlapping consensus in their respective societies.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.036
Scholarly communication0.0120.008
Open science0.0010.004
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.043
GPT teacher head0.236
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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Same venueCambridge University Press eBooksSame topicAmerican Constitutional Law and PoliticsFrench-language works237,207