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Record W4404859902 · doi:10.3390/rel15121457

Liberal Citizenship Through the Prism of Shia Jurisprudence: Embracing Fundamental over Partial Solutions

2024· article· en· W4404859902 on OpenAlexaff
Javad Fakhkhar Toosi

Bibliographic record

VenueReligions · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsJurisprudenceCitizenshipPrismPolitical scienceLawSociologyPoliticsPhysicsOptics

Abstract

fetched live from OpenAlex

This article explores the compatibility of liberal citizenship with Twelver Shia jurisprudence, a topic previously analyzed from the perspective of Sunni schools, most notably in the extensive research of Andrew F. March. This study confronts the challenges of reconciling liberal citizenship with Islamic jurisprudence, as highlighted in March’s work, through the lens of Shia legal thought. Rather than aiming to critique or review March’s research, this article considers his work solely as a representative example addressing the topic from the perspective of Sunni jurisprudence. This approach provides readers with a fundamental contrast, illuminating the unique insights that emerge from examining the subject within the framework of Shia jurisprudence. Unlike Sunni jurisprudence, which addresses these issues case-by-case by reviewing relevant Quranic and narrational sources, Twelver Shia jurisprudence offers a more foundational resolution. Owing to the belief in the occultation of the twelfth Imam and its implications for the implementation of Islamic law, Shia scholars have advanced theories such as the theory of obstruction (insidād) and the suspension of the social and political dimensions of Sharia. These theories effectively narrow the scope of Sharia, allowing for the acceptance of laws from non-Islamic states and circumventing potential conflicts with liberal citizenship in the absence of the twelfth Imam.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.375
Teacher spread0.297 · 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 teacher head, not a consensus.

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

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

Citations0
Published2024
Admission routes1
Has abstractyes

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