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Record W2552935272 · doi:10.1080/02185377.2016.1245153

Ummah or tribe? Islamic practice, political ethnocentrism, and political attitudes in Indonesia

2016· article· en· W2552935272 on OpenAlexaff
Nathan W. Allen, Shane Joshua Barter

Bibliographic record

VenueAsian Journal of Political Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsEthnocentrismPoliticsEthnic groupIslamRespondentSocial psychologyPreferencePolitical scienceSociologyPsychologyLawEconomicsGeography

Abstract

fetched live from OpenAlex

Existing research has uncovered a link between religious practice and political ethnocentrism. Religious individuals are relatively inclined to both support policies that benefit their own ethnic group and support political competitors seeking to represent them. These findings are broadly consistent with a large body of literature that examines the relationship between religion and ethnic prejudice. To date, empirical research has concentrated overwhelmingly on Western, Christian contexts. There is, however, reason to believe that Islamic practice may produce more universalistic beliefs and attitudes. This paper examines the relationship between religious participation and political ethnocentrism in Indonesia, this world’s largest Muslim-majority country. Using survey data collected during the lead-up to the 2009 national elections, this paper examines the relationship between religious practice and expressed preference for co-ethnic political leadership. It finds that a respondent’s self-reported level of religious activity strongly correlates with stated preference for co-ethnic leadership. These findings bolster confidence that the relationship between religious participation and ethnocentrism holds beyond Western Christian contexts. For Indonesia, deepening Islamic practice could thus predict a rise in ethnocentrism, threatening the country’s reputation for tolerance.

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)0.003
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
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 score0.861
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.007
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.024
GPT teacher head0.362
Teacher spread0.338 · 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.

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
Published2016
Admission routes1
Has abstractyes

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