From Patronage Machine to Partisan Melee: Subnational Corruption and the Evolution of the Indonesian Party System
Bibliographic record
Abstract
The party system in Indonesia has expanded in the post-Suharto era. With each successive election, voters have spread their support across a wider array of parties. This has occurred despite deliberate institutional tweaks designed to consolidate the system by privileging large parties. Why has the party system expanded despite increasing institutional incentives to consolidate? This article places party system change in a broader context of decentralization and corruption. The decentralization and deconcentration of political power has opened multiple avenues for voters and elites to access state resources. Whereas major parties were expected to dominate resources in the immediate aftermath of the transition, changes to the formal and informal institutions eroded their control over the state. This has caused previously consolidated subnational party systems to fracture. The argument is demonstrated using narrative and newly constructed cross-district datasets. The paper develops the concept of rent opportunities, defined as the ability to access and abuse state resources. Party system expansion has been greatest in areas with high rent opportunities, where both voters and elites are particularly motivated by the competition for state resources. In these areas, characterized by large state sectors, the formerly authoritarian party (Golkar) initially won large electoral victories due, in part, to its control over patronage. As Golkar lost its ability to monopolize resources, the party system fractured. Voting for small parties surged and the party machine was replaced by a partisan melee. My argument exposes the limits of institutional engineering and underlines the formative role corruption has had on the evolution of Indonesia’s party system.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".