Ethnic Fractionalization and Polarization in New Capital City of Nusantara, Indonesia: Analysis of Potential Conflict
Bibliographic record
Abstract
This study analyzes ethnic fractionalization and polarization in the new Indonesian Capital of Nusantara (IKN), with a focus on the potential for ethnic conflict as the government directs significant resources towards its development. Data was collected from 54 villages within IKN territory over five months and analyzed quantitatively. The findings show a high ethnic fractionalization index of 0.79 and a slightly high polarization index of 0.61, indicating a moderate risk of conflict. However, the ethno-demographic and ethnopolitical conditions remain conducive to supporting IKN's development. While previous research has addressed ethnic conflict in Indonesia, few studies have examined its implications for major national projects like IKN's development. This study offers a new quantitative perspective on how ethnic diversity influences large-scale governmental projects, highlighting the role of ethnic fractionalization and polarization in shaping the stability of IKN's development. Although such a polarization index indicates the potential for conflict being slightly high, the ethno-demographic and ethnopolitical condition in IKN is still relatively conducive to providing supporting capacity to IKN development, viewed from a statistical and ethnopolitical perspective.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".