Effect of Farmland Transfer on Poverty Reduction under Different Targeted Poverty Alleviation Patterns Based on PSM-DID Model in Karst Area of China
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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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".