Worlding Water Supply: Thinking Beyond the Network in Jakarta
Bibliographic record
Abstract
This article draws on scholarship in Southern theory to ‘world’ the study of water’s urbanization. This means complicating scholarship by widening the focus beyond the application of Northern norms to engage with complex and diverse practices in Southern cities. For water’s urbanization, this means focusing on what water supply is for the majority: neither the centralized piped‐water network nor its absence, but the range of practices and technologies that unite people, nature and artefacts in a complex socio‐ecological politics of water. Drawing on scholarship from Southern urbanisms, urban political ecology, and science and technology studies, we illustrate how expanding water’s urbanization to include more than networked infrastructure in Jakarta draws attention to the importance of ecological connections between piped water, groundwater, wastewater and floodwater. Thinking beyond the network requires deeper engagement with the ecological connections between the diverse flows of water in and around urban environments. These produce distinct forms of fragmentation that are missed when analysis is limited to piped‐water supply. The emphasis on ecological connections between flows of water and power seeks to draw attention back to the importance of the uneven exposure to environmental hazards in cities in which neither water nor nature are wholly contained by infrastructure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".