Political Leadership and Civilian Supremacy in Third Wave Democracies: Comparing South Korea and Indonesia
Bibliographic record
Abstract
Introduction The praetorian problem how to establish and maintain control over the armed forces by elected officials is one of the most critical challenges facing Third Wave democracies.1 Among East Asian cases, South Korea appears to have successfully met this challenge, while Indonesia has not. In South Korea, a highly politicized armed forces faction was effectively subdued, two former presidents were jailed and the role of military intelligence was severely downgraded. Most observers now believe there is litde chance of a military return to power.2 In Indonesia, by contrast, the armed forces retain most of their predemocratic position and perquisites, including the territorial system of military commands parallel to the civilian government hierarchy, and de facto control over their own budget. Many observers believe that a return to power remains a real, if hard to measure, possibility.3 What accounts for this difference? Structurally, Korea seems much readier for democracy than Indonesia.4 Korea has a more developed economy, stronger state, more educated population, larger middle class and more vigorous civil society. Korea's territory is more compact and its population less complex ethnically, with fewer politicized religious divisions. Koreans have long been a single natipn, while Indonesia is a twentieth-century creation. For those who stress the role of foreign actors, the United States
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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 | 0.002 |
| 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) | 0.008 | 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".