Factors Affecting the Incomes of Peasant Households Affected by Climate Change in Tra Vinh Province, Viet Nam
Bibliographic record
Abstract
The results of this study aim to propose implicational policies for the People's Committee of Tra Vinh province, Viet Nam; Department of Agriculture and Rural Development of Tra Vinh province and related departments in Tra Vinh province. Thanks to these results, they may have effective solutions to support peasants who can have new circumstances in the areas of climate change due to adaptive salinization. It can also be stated that it is very necessary to find solutions to raise their incomes, improve better lives in the future. Studying factors on incomes of peasants’ households affected by climate change by direct interview method of 280 households, in 07 districts including: Tra Cu, Cau Ngang, Chau Thanh, Duyen Hai, Tieu Can, Cau Ke and Tra Vinh city in the regions affected by climate change in Tra Vinh province. By using multivariate regression method, the authors have found out the factors affecting peasants’ income in these regions: Diversifying income generation activities, demanding for bank loans, education, work experience and land areas of households. From the research results, it can be proposed the solutions to increase peasants’ income to adapt to climate change in these areas in the future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".