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Record W2790795661 · doi:10.1080/20566093.2018.1439808

Sufi articulations of civility, globality, and sovereignty

2018· article· en· W2790795661 on OpenAlexaff
Armando Salvatore

Bibliographic record

VenueJournal of Religious and Political Practice · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsMcGill University
Fundersnot available
KeywordsCivilitySovereigntyGlobalitySociologyColonialismPolitical scienceState (computer science)Political economyUniversalismGlobalizationLawPolitics

Abstract

fetched live from OpenAlex

This article provides an analysis of Sufi life and organization, combining historical depth and theoretical awareness. It investigates how Sufism emerged as an urban phenomenon. Sufi brotherhoods were at the forefront of a proto-globalization based on a hemisphere-wide networking between metropolitan regions, rural provinces, and nomadic formations. Furthermore, cities became nodes within wider circulations, rather than, as in European and Weberian models, centers of corporate powers. The emerging patterns of civility were open-ended, balancing inner cultivation, communicative skills, and outward etiquette. The article shows how this global civility translated into original conceptions of sovereignty that were more malleable than those of the European Leviathan. A millenarian universalism imbued with Sufi saintliness bolstered the centralized sovereignty of early modern Muslim empires. Sufi contributions to these empires nurtured a cosmopolitan culture, facilitating commercial exchange and intellectual connectedness between Europe and China. When Europe rose to global hegemony, neo-Sufi movements engaged in state-building processes which challenged European colonial presence. The article concludes by exploring how post-Sufi developments within Muslim-majority postcolonial societies re-oriented state power and led to the emergence of a trans-territorial notion of sovereignty.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.830
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.352
Teacher spread0.333 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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