Effect of Farmland Transfer on Poverty Reduction under Different Targeted Poverty Alleviation Patterns Based on PSM-DID Model in Karst Area of China
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Bibliographic record
Abstract
Rural farmland transfer is a key factor in the successful implementation of targeted poverty alleviation strategies in China. In this paper, a multidimensional index system with 15 indicators from five dimensions, namely, natural, human, physical, financial, and social capital was established. It analyzed the effect of farmland transfer on poverty alleviation under four typical poverty alleviation models implemented in a karst area in China by using Propensity Score Matching (PSM) and Difference-in-Difference (DID) to analyze 467 rural households questionnaire responses from five representative villages in Guizhou Province. The results show that different models had different effects on poverty reduction. In the model of "three changes" + relocation for poverty alleviation + rural tourism + poor households, farmland transfer was the most effective in poverty alleviation, as attested by its average treatment effect on the treated (ATT) value of 0.44. Rural households' nonfarm income increased significantly to develop rural tourism after relocation from inhospitable areas. In the model of "farmland lease/shareholding" +cooperative + enterprise + poor households, farmland transfer had a moderate effect on poverty alleviation, with an ATT value of 0.06. Its effect on the total income of rural households was the lowest among the four models. This study's results can provide a theoretical reference for solidifying the benefits of poverty alleviation and rural revitalization strategies in karst areas.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it