Water, Water Everywhere: Toward Participatory Solutions to Chronic Urban Flooding in Jakarta
Bibliographic record
Abstract
Jakarta has entered an era of chronic flooding that is annually affecting tens of thousands of people, most of whom are crowded into low-income neighbourhoods in flood-prone areas of the city. As the greater Jakarta mega-urban region—Jabodetabck—approaches the 30 million population mark and the sources of flooding become ever more complex through combinations of global climate change and human transformations of the urban landscape, government responses to flooding pursued primarily through canal improvements fall further behind rising flood risks. Years of field observation and archival and ethnographic research arc brought together in a political ecology framework to answer key questions concerning how government responses to flooding continue without significant participation of affected residents, who arc being compelled to relocate when floods occur. How do urban development processes in Jakarta contribute to chronic flooding? How does flooding arise from and further generate compound disasters that cascade through Jakarta’s expanding mega-urban region? What is the potential for neighbourhoods and communities to collaboratively respond through socially and environmentally meaningful initiatives and activities to address chronic flooding? Floods, urban land use changes, spatial marginalization, and community mobilization open new political dynamics and possibilities for addressing floods in ways that also assist neighbourhoods in gaining resilience. The urgency of floods as problems to be solved is often interpreted as a need for immediate solutions related only to flood management, but community resilience is more crucially attained in non-emergency times by expanding rights to dwell in this city, build houses, and create vernacular communities, livelihoods, and social support networks.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".