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Record W4321788699 · doi:10.5509/20239615

Comparing Religious Intolerance in Indonesia by Affiliation to Muslim Organizations

2023· article· en· W4321788699 on OpenAlexvenueno aff
Hariyadi Hariyadi, Akhmad Rizal Shidiq, Arief Anshory Yusuf, Dharra Widdhyaningtyas Mahardhika

Bibliographic record

VenuePacific Affairs · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsIndonesianIslamOrdered logitScale (ratio)Religious organizationPolitical scienceSociologySocial psychologyPublic relationsPsychologyGeographyLawStatistics

Abstract

fetched live from OpenAlex

Very few studies explicitly, let alone quantitatively, examine gaps in religious intolerance among individual Muslims based on a liation with major Muslim organizations in Indonesia. Most existing studies either focus on a single organization (non-comparative), are at the organizational policy level (not examining individual attitudes), or use a limited number of samples in their analysis. Against this backdrop, this study compares Indonesian Muslims' levels of religious intolerance based on their a liation with Muslim organizations or traditions: Nahdlatul Ulama (NU), Muhammadiyah, and other organizations. We utilize a large-scale household survey, the 2014 Indonesia Family Life Survey-5, and run an ordinal logistic regression to identify organizations' rank on the religious intolerance scale. We find that Muslims without any a liation with a Muslim organization (some 18 percent of Indonesian Muslims) are the most tolerant. Against this reference group, we find that NU followers are generally the most tolerant, followed by those a liated with Muhammadiyah, and those a liated with other Muslim organizations. This finding adds a stock of knowledge to our understanding of religion and society, especially regarding interfaith relations in Indonesia and in the Muslim world in general. Methodologically, this study also shows the benefit and feasibility of identifying the dynamic of religious intolerance using a quantitative approach at a micro level.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.250
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.015
GPT teacher head0.269
Teacher spread0.254 · 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 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
Published2023
Admission routes1
Has abstractyes

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