Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".