Cross-Party Presidential Dynasticism in Indonesia: Evidence from the 2024 Presidential Elections
Bibliographic record
Abstract
Dynasticism is a persistent feature of global politics, and the political role of elite families and their client networks continues to evolve in Indonesia, the Philippines, Thailand, and elsewhere in Southeast Asia. In Indonesia, former President Jokowi's political dynasty, consisting of family members and clientelistic elites, is causing a number of contortions to the constitutional and presidential system. This article analyzes the strategy used by Jokowi to outmanoeuver the leading party in his coalition, the Indonesian Democratic Party of Struggle (PDI-P), while forming pragmatic big-tent coalitions through the co-optation of opposition parties and leaders. Indonesia's post-1998 democratic system has structural flaws and legal ambiguities that enable the president to expand constitutional powers and influence key institutions. This article contends that a novel form of cross-party presidential dynasticism is reshaping elite power and electoral coalition- building in Indonesia. Our analysis is based on interviews with political party elites and campaign managers who were closely involved in the 2024 Indonesian presidential elections, supplemented by a review of Indonesian-language scholarship and reporting. Our findings contribute to theories of promiscuous power-sharing and coalition presidentialism. The rise of electoral manipulation and cross-party deal-making raises questions about the integrity of the world's third-largest democracy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".