Introduction: Governing Flooding in Asia's Urban Transition
Bibliographic record
Abstract
The twenty-first century not only marks the advent of Asia’s first urban era in which more than half of its population lives in cities; it is also the emergence of an age of increasing frequency and intensity of environmental disasters. Urban flooding leads disaster trends and is directly impacting the lives and livelihoods of a growing share of Asia’s population. By 2010 more than 1.5 billion people were residing in urban areas in Asia, accounting for over half of the global urban population. The pervasive coastal and riparian orientation of Asia’s rapid urban transition is placing greater numbers of people in locations that arc highly exposed to floods, cyclones, tropical storms, and tsunamis. Human transformations of the natural and built environment of cities substantially add to global climate change as interactive sources of the heightening occurrence of floods. Moreover, floods contribute to compound disasters that generate cascading effects with multiple sources, interactive impacts, and long-term social and economic recover)’ issues. The pervasive and socially uneven impacts of floods bring acute awareness of flooding as a political issue for participatory governance. In light of these interwoven complexities, responses can no longer be carried out as sector management tasks, but must instead adopt multi-sector, multi-disciplinary, and multi-stakeholder approaches to disaster governance to directly link knowledge to action in preparing for, responding to, and recovering from floods in urbanizing Asia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".