Toward Integrated Disaster Risk Management in Vietnam : Recommendations Based on the Drought and Saltwater Intrusion Crisis and the Case for Investing in Longer-Term Resilience
Bibliographic record
Abstract
Vietnam is one of the most hazard-prone \n countries in the East Asia and Pacific region, with \n droughts, severe storms, and flooding causing substantial \n economic and human losses. Climate change is projected to \n increase the impact of disasters, especially the timing, \n frequency, severity, and intensity of hydro-meteorological \n events. Vietnam’s 2015–2016 drought and associated saltwater \n intrusion (SWI) offer a preview of what could become the new \n normal, and make clear the need to take action to ensure the \n country’s economic and societal well-being. SWI developed \n into a national crisis, with close to two million people \n affected due to damaged livelihoods and the country seeking \n international help. This report takes a deeper look at the \n drought and SWI crisis faced by Vietnam, identifies the gaps \n across key sectors, and recommends the principal short and \n longer-term actions needed for integrated disaster risk \n management. The recommendations are based on global \n experiences in good governance with intersectoral \n coordination in disaster forecast and early warning, and in \n community empowerment in water resource management and \n agricultural production.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".