Rematch: Islamic politics, mobilisation, and the Indonesian presidential election
Bibliographic record
Abstract
Indonesia’s 2019 presidential election brings a rematch between incumbent Joko Widodo and Prabowo Subianto, though against a backdrop of increasingly active conservative Islamic movements. Analyses of this contest – as well as of contemporary Indonesian politics more generally – are often based on assumptions around which constituencies matter and which political factions they support. This paper examines those assumptions using an original dataset of fine-grained returns and census data, including a latent variable to capture the independent effect of Islamic conservatism. We find that conservative Muslim areas overwhelmingly supported Prabowo in 2014, but turned out in relatively low numbers. By contrast, rural poor areas turned out heavily for Widodo. This suggests that the conservative vote was under-mobilised and has a greater electoral potential than previously demonstrated. Given the recent mobilisation by conservative segments in society, observers should be prepared for significant shifts in the Indonesian electorate in 2019 and beyond.Abbreviations: NU: Nahdlatul Ulama; FPI: Islamic Defenders Front
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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 | 0.002 |
| 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) | 0.006 | 0.001 |
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.
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".