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Record W4394819347 · doi:10.1017/s1755048324000087

Explaining the distinction between religious and political activism in Islamism: evidence from the Tunisian case

2024· article· en· W4394819347 on OpenAlexaff
Fabio Merone, Rory McCarthy

Bibliographic record

VenuePolitics and Religion · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMulticulturalism, Politics, Migration, Gender
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPoliticsPolitical economyIdeologySocial movementPolitical scienceAmbiguityPolitical activismDemocracyArgument (complex analysis)SociologyLaw

Abstract

fetched live from OpenAlex

Abstract Tunisia's Islamist movement Ennahda has announced a separation of political and religious work, apparently reinforcing a “post-Islamist” argument that Islamic parties have left behind religious mobilization. However, the boundary between religious and political fields is highly porous. We ask why the distinction between religious and political activism remains a point of ambiguity within Islamism. Drawing on semi-structured interviews with 48 men and women who participated in the movement in the 1970s and 1980s in Tunis and Sousse, we develop a microlevel explanation of Islamist mobilization. We argue that religious and social Islamist activism is replete with political intent, which worked through three mechanisms: a counter-hegemonic ideology, an activist engagement in social transformation, and a formal organization. These findings add empirical insights to the case of Ennahda, provide leverage in explaining the politicization of Salafist movements, and underscore the legacy of asymmetric party capacities in shaping outcomes in a democratic transition.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0070.009
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.113
GPT teacher head0.402
Teacher spread0.289 · 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 designQualitative
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

Citations4
Published2024
Admission routes1
Has abstractyes

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