Agriculture and Climate Change: Perceptions of Provincial Officials in Vietnam
Bibliographic record
Abstract
Climate change is expected to have serious impacts on developing countries, including Vietnam. The central government of Vietnam has launched programs to study climate change trends and impacts on natural resources, environment and socio-economic development, and adaptation strategies. These programs have the active involvement of many ministries, sectors, research institutions and local governments. This paper addresses theperceptions of provincial officers in Vietnam regarding climate change, its impacts on agricultural activities, and adaptation options. It examines the current knowledge and understanding capacity of provincial officials in implementing the National Target Program to Respond to Climate Change, and the Action Plan to Response to Climate Change of the Agriculture and Rural Development Sector. The results from the study provide insight into the perceptions on climate change and climate change adaptation measures held by Vietnamese government officials working in environmental and agricultural sectors. The survey data indicate that Vietnamese government officials are aware of climate change and its potential impacts, but have relatively poor understanding of some aspects, given the key role of government officials in implementing Vietnamese adaptation policies and mitigation measures. These new findings are important to Vietnamese and international organizations involved in assisting agricultural research and extension agencies with identifying and implementing strategies to adapt Vietnamese farming systems to a changing climate.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 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".