Liberal Citizenship Through the Prism of Shia Jurisprudence: Embracing Fundamental over Partial Solutions
Bibliographic record
Abstract
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.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.088 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".