Capital city relocation in Indonesia: compromise failure and potential dysfunction
Bibliographic record
Abstract
The relocation of Indonesia’s capital city from Jakarta to Nusantara in East Kalimantan, as proposed by President Joko Widodo, represents a profound shift in the nation’s urban and political geography. This initiative, founded on development equity and national unity goals, seeks to shift from an ‘evolved city’ framework to a ‘designed city’ model. However, the rapid decision-making process, which lasted only 43 days, and the apparent lack of inclusive public deliberation and participation in critical decisions raises concerns about the democratic underpinnings of this endeavour. Historical precedents from countries such as the United States, Canada, and Australia highlight the benefits of a democratic approach to capital determination, while examples from Nigeria highlight the risks associated with non-democratic processes. For Indonesia, ensuring a democratic, participatory, and inclusive approach is critical not only for successfully relocating the capital but also for preserving the integrative, symbolic, and cultural functions of a capital city. In Indonesia, the hasty passage of the capital city law jeopardizes not only the successful implementation of the relocation but also the integrative, symbolic, and cultural roles that a capital city should play. This paper contends that the absence of democratic and spatial compromise could jeopardize the relocation’s intended goals, putting Indonesia’s new capital’s functional efficacy at risk.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".