Analysis on the Influencing Factors of Farmers’ Satisfaction to Vouchers--Base on FAO Post-earthquake Assistance Program in Sichuan, China
Bibliographic record
Abstract
After the Wenchuan earthquake in Sichuan, China, the Food and Agriculture Organization of United Nations launched the emergency assistance and restoration programs through the approaches of Direct Inputs Distribution (DID) and Agricultural Inputs Voucher (AIV). After the investigation of the 204 AIV beneficial households randomly selected in the 2 pilot counties, the paper analyzed the beneficial farmers’ satisfaction of the AIV program and the influencing factors to their satisfaction. The logistic model was applied to detect the influencing factors. The results we got through the software of E-Views 5.0 showed that the gross income, the area of the arable land, the variety can be purchased and the procedure of the voucher using were playing important roles to affect the beneficial farmers’ satisfaction to vouchers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".