Waste accumulation in Jakarta’s slums: Neoliberal flows of waste distribution
Bibliographic record
Abstract
Urban slums in the Global South have a formidable challenge of mismanaged waste. Behind this challenge lies urban politics, creating disproportionate exposure to waste in marginalized settlements. This paper articulates an urban political ecology of uneven waste accumulation in slums with a case study of Jakarta, Indonesia. I apply a mixed-methods approach, by integrating a spatial regression model with a critical qualitative analysis, to draw the connection between the slums’ waste crisis and neoliberal waste infrastructure. Since the mid-2010s, Jakarta’s waste governance has shifted from a conventional collect-transport-dispose model to a circular economy model operating under a technocratic and neoliberal economy. Under the shifted governance, the informal sector has been strengthened through the integration of high-tech infrastructure to reduce waste and extract new profits from materials. However, the sector has been unevenly strengthened across the city, creating two flows of waste accumulation activities. First, the strengthening has been accelerated in low-income residential areas by introducing financial incentives for the informal sector to make up the shortfall created by public services. Consequently, the informal sector in slums—with its limited focus on recyclable materials—has led to the accumulation of mismanaged non-recyclable waste in unregulated dumpsites of slums. On the other hand, public services are relatively prevalent in capital-intensive areas to promote the economic growth. However, due to its compact land-uses in those areas, the services utilize the unregulated dumpsites in slums as semi-permanent landfills for storing the collected waste.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".