Urban climate governance in Southeast Asian small and mid-sized cities: undermining resilience and distributing risks unevenly
Bibliographic record
Abstract
Secondary cities are home to most of the world’s urban populations vulnerable to climate change, yet researchers and policymakers have devoted less attention to them than large and megacities. To help address this gap, this paper explores the relationship between incomplete decentralized governance, climate change, and urban resilience. It does through the case studies of secondary cities of Cambodia, Myanmar, Thailand, and Vietnam. Secondary cities are of importance because they are the fastest growing cities in the Global South but also because they have weaker capacity to address climate risks. Through these case studies, the paper draws comparisons between the different cases to look at the linkages between decentralization and urban resilience in secondary cities. Overall, it argues that climate governance, due to the retention of power and resources by central bureaucrats along with fragmented governance structures, and misaligned incentive structures which prioritize economic growth over climate protection have undermined resilience building and contributed to the uneven distribution of climate risks in these cities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".