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Record W2889025432 · doi:10.1177/0192512118784225

Revisiting the Islamist–Secular divide: Parties and voters in the Arab world

2018· article· en· W2889025432 on OpenAlexaff
Eva Wegner, Francesco Cavatorta

Bibliographic record

VenueInternational Political Science Review · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsIdeologyPolitical economyPolitical sciencePoliticsSurvey data collectionVotingSecularismWorld Values SurveySociologyPositive economicsLawEconomics

Abstract

fetched live from OpenAlex

Electoral politics in the Arab world are either portrayed as clientelistic affairs void of content or as highly ideological clashes between Islamist and Secular Left forces. Although both arguments are intuitively appealing, the empirical evidence to date is limited. This article seeks to contribute to the debate by investigating the extent of programmatic voter support for Islamist and Secular Left parties in seven Arab countries with data from recent surveys by the Arab Barometer, Afrobarometer and World Values Survey. Ideological congruence between voters and parties exists but is limited to the Islamist–Secular core divide with regard to the role of religion in politics and gender values. In contrast, there are virtually no differences in economic attitudes between respondents and there is no evidence of class-based voting, with Islamist and Secular Left parties sharing the same voter base of better-off, more educated voters. Core results are robust across surveys.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.047
GPT teacher head0.395
Teacher spread0.348 · 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 designObservational
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

Citations33
Published2018
Admission routes1
Has abstractyes

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