Farming Households' Perception on Natural Disaster Impacts to Livelihoods and Adaptation Practices: A Case Study of Coastal Provinces in Central Vietnam
Bibliographic record
Abstract
Vietnam is one of the countries most affected by natural disasters in the Asia Pacific. In the context of climate change, natural disasters tend to increase in both frequency and intensity, negatively affecting the livelihoods of communities, especially poor farmers. In Vietnam, the central region is identified as the most vulnerable to natural disasters, especially for poor households with high exposure and low adaptive capacity. This study was conducted in 4 coastal provinces in Central Vietnam to evaluate farmers' perception of natural disaster impacts on livelihoods and their adaptation practices. We employed the analytical framework by DFID and IPCC for households’ capital and livelihood assessment in combination with perception evaluation approach from literature. The method used for analysis include quantitative and qualitative approaches. A survey of 444 farming households randomly selected to collect primary data. In addition, indepth interviews with key informants were also carried out to get more insight of the story. Research results show that local people are quite aware of the change in frequency and intensity of natural disasters. Despite fairly good perception of the impacts of natural disasters, the adaptive capacity of farmers is low due to the lack of adaptive capital, including physical, natural, human, financial, and social capital. Faced with natural disasters, people have taken many adaptation measures to maintain their livelihoods, including indigenous knowledge. The study also shows farmers need support to strengthen their disaster resilience through access to information, knowledge, technology, and financial capital. In addition, the link between livelihoods and climate change should also be further developed with different dimension so that a full picture is formed for proper management strategies.
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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 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".