Frontier Urbanisation in East Kalimantan: Displacement, Livelihood Shifts, and Contested Development in Indonesia’s New Capital
Bibliographic record
Abstract
Urbanization in the Global South is increasingly influenced by large-scale infrastructure and frontier development, frequently exacerbating disparities and displacing marginalized populations.This paper situates Indonesia's new capital (IKN) within debates on neoliberal urbanism and resource extraction, examining how long-established resource-based communities in East Kalimantan perceive IKN's urbanisation.Research was conducted in a village within IKN's official delineation, using surveys, interviews with 74 local and government informants, and field observation (Aug 2023-Mar 2025)-thematic analysis, complemented by government and NGO reports, compared planned and actual outcomes.While state and corporate narratives present IKN as a green and inclusive modernisation engine, communities historically reliant on mining, plantations, and agriculture express ambivalence.Indigenous informants emphasise the loss of customary land rights through coercive acquisitions and insufficient compensation, exacerbated by post-IKN land price inflation.Migrant labourers view IKN as an economic opportunity but face low wages and exclusion from skilled positions, reinforcing labour hierarchies.The paper frames IKN as frontier urbanisation reproducing historical patterns of accumulation by dispossession, without addressing structural inequalities.The findings enhance discussions on Global South urbanism, promoting policies that support indigenous sovereignty and ensure equitable distribution of the benefits of urbanization.
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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 | 0.000 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".