‘The weather is like the game we play’: Coping and adaptation strategies for extreme weather events among ethnic minority groups in upland northern Vietnam
Bibliographic record
Abstract
Abstract The Vietnamese government, along with country‐based non‐government organisations, are well aware of the vulnerability of Vietnam's coastal and low‐lying areas to extreme weather events. Yet scant attention has been paid to extreme weather hazards affecting Vietnam's northern mountainous regions and the livelihoods of ethnic minority farmers residing there. Building on conceptual tools from vulnerability, food security and sustainable livelihoods literatures, we examine the impacts of extreme weather, namely drought and severe cold spells, in Vietnam's northern uplands. We explore the degree to which these events impact the livelihood portfolios and food security of ethnic minority farmers, and examine the coping strategies households initiate, based on their ecological knowledge as well as recent market integration initiatives. Drawing on ethnographic fieldwork with ethnic minority Hmong and Yao semi‐subsistence households undertaken yearly from 2012 to 2014, we demonstrate that financial capital – now more central to households' livelihoods than ever before due to state‐sponsored agricultural intensification – is an important means for farmers to cope with extreme weather events. Yet concurrently, longstanding culturally rooted social capital, networks and ties remain critical. Nonetheless, short‐ and long‐term adaptation is not widespread, leading us to investigate possible explanations.
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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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".