Risky Change? Vietnam's Urban Flood Risk Governance between Climate Dynamics and Transformation
Bibliographic record
Abstract
Vietnam's cities are not only rapidly transforming along with the country's politico-economic change but are also recognized by various studies as being increasingly exposed to natural hazards and the projected impacts of climate change. This results in substantial challenges for urban disaster risk governance which are, however, not well understood scientifically and underemphasized politically. Against this background, the paper traces the dynamics in urban vulnerability and explores how the responsibilities and capacities for risk reduction and adaptation are negotiated and shared between state and non-state actors within the country's changing political economy. The city of Can Tho, the demographic and economic centre of the highly flood- and typhoon-prone Mekong Delta, serves as an in-depth case study, drawing on 12 months of empirical research by the author. The findings suggest that the transformation process has not only yielded ambiguous and socially stratified vulnerability effects amongst urban residents; it has also resulted in significant shifts in the way that different stakeholders frame and attribute risk management. Despite the continued paternalistic rhetoric of the party-state apparatus as caretaker, considerable mismatches between state and non-state adaptation action can be observed, potentially undermining the effectiveness of both realms. The findings therefore call for a paradigm shift in Vietnam's urban disaster risk governance. Future approaches need to go beyond the adjustment of physical infrastructure. Rather, the institutional configuration of risk governance itself needs to be adapted in order to mediate and integrate different types of risk reduction measures. These unfold across the increasingly divergent range of urban actors and their interests in terms of spatial scales, temporal scales, normative motivations, and capacities.
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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 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".