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Record W3003910936 · doi:10.5509/202093131

Inequality and Democratic Support in Indonesia

2020· article· en· W3003910936 on OpenAlexvenueno aff
Burhanuddin Muhtadi, Eve Warburton

Bibliographic record

VenuePacific Affairs · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsDemocracyInequalityOpposition (politics)Economic inequalityPresidencyIdeologyPoliticsWorld Values SurveyPolitical sciencePolitical economySurvey data collectionDevelopment economicsStatus quoDemographic economicsSociologyEconomicsLaw

Abstract

fetched live from OpenAlex

Indonesia is a country of significant inequalities, but we know little about how Indonesians feel about the gap between rich and poor. Comparative research suggests that negative perceptions of inequality can erode public support for democratic institutions. Using survey data, we explore the relationship between inequality and support for democracy in Indonesia. We find Indonesians are divided in their beliefs about income distribution. But this variation is not determined by actual levels of inequality around the country, nor by people's own economic situation; instead, political preferences and partisan biases are what matter most. Beliefs about inequality in Indonesia have become increasingly partisan over the course of the Jokowi presidency: supporters of the political opposition are far more likely to view the income gap as unfair, while supporters of the incumbent president tend to disagree—but they disagree much more when prompted by partisan cues. We also find that Indonesians who believe socio-economic inequality is unjust are more likely to hold negative attitudes toward democracy. We trace both trends back to populist campaigns and the increasingly polarized ideological competition that marked the country's recent elections. The shift toward more partisan politics in contemporary Indonesia has, we argue, consequences for how voters perceive inequality and how they feel about the democratic status quo.

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)0.000
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: none
Teacher disagreement score0.988
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

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

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 head0.029
GPT teacher head0.270
Teacher spread0.241 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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