VOTE BUYING IN INDONESIA: CANDIDATE STRATEGIES, MARKET LOGIC AND EFFECTIVENESS
Bibliographic record
Abstract
Abstract What underlying logic explains candidate participation in vote buying, given that clientelist exchange is so difficult to enforce? We address this question through close analysis of campaigns by several dozen candidates in two electoral districts in Java, Indonesia. Analyzing candidates’ targeting and pricing strategies, we show that candidates used personal brokerage structures that drew on social networks to identify voters and deliver payments to them. But these candidates achieved vote totals averaging about one quarter of the number of payments they distributed. Many candidates claimed to be targeting loyalists, suggestive of “turnout buying,” but judged loyalty in personal rather than partisan terms, and extended their vote-buying reach through personal connections mediated by brokers. Candidates were market sensitive, paying prices per vote determined not only by personal resources, but also by constituency size and prices offered by competitors. Accordingly, we argue that a market logic structures Indonesia's system of vote buying.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".