What motivates farmers’ decision to organic farming conversion: The case of conventional mango farming in Vietnam
Bibliographic record
Abstract
This research is aimed at analyzing perception and identifying determinants of the decision to convert to organic mango farming in Mekong Delta (MD) Vietnam. The research was conducted by using a direct survey data set from 109 household heads in this region collected by stratified random sampling method. The research method used was descriptive statistics and the binary Logit model. The research results revealed some interesting points. In the total observations gathered, only about half of households decided to convert, mainly due to local implementation and awareness of safety for consumers and environmental protection. Still, the most important reason for farmers to convert was to get a higher selling price. The binary Logit model analyzing the determinants found that the older the farmer, the more difficult it is to decide to convert. At the same time, training and enhancing awareness about organic farming will increase the probability of conversion decisions. Based on the research results, several relevant solutions on investment, production linkage, and propaganda to raise people's awareness were recommended, thereby increasing the probability of deciding to convert.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".