Tying Our Scooters Down: Urban Governance Of Flooding In Bago, Myanmar
Bibliographic record
Abstract
Urban flooding poses significant challenges to cities in Southeast Asia including loss of life, human displacement, and damaged infrastructure. As cities in the region grow and as the effects of climate change worsen, urban flooding is becoming more frequent and severe. As such, both local governments and international networks have taken on the task of preparing for and managing urban floods. This research situates flood governance in Bago City, Myanmar in the literature on environmental governance and urban political ecology, investigating how local governance actors interpret the significance of flooding and how they are influenced by the discourse of international networks that promote urban climate governance. Using the 2015 Bago floods as a point of entry, results were derived from semi-structured interviews with (10) government officials and a document review of (4) international networks with diverse structures and goals, as well as (23) key informant interviews. Broadly, this research found that government officials interpreted the 2015 floods as extreme but also as an example of the government's increasing capacity to respond to disasters, that local and regional governments lack the human and capital resources to take on the greater responsibility for flood management that they wish to, that government often fails to act on their knowledge about external causes of flooding such as land use and climate changes, and that government officials strategically adopt neoliberal paradigms advanced by international networks while reinterpreting them to advance their own goals of expanding the role of the state. This research also found that government officials often failed to acknowledge the pre-existing community methods of flood management and risked undermining those methods through their interventions. Analysis considers how critical resilience, vulnerability, and adaptation perspectives were absent from the flood governance discourse in Bago, and further, that research challenging the ideological assumptions of the international networks was not acknowledged in their knowledge production. This thesis posits that although the discourses of the government driven international networks and the private-sector foundation driven networks were not engaged in conversation with one another, urban climate impacts in the global south could serve as a catalyst for productive debate over the application of global climate justice frameworks to the local scale.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".