Impacts of Eco-Poverty Alleviation Policies on Farmer Livelihood Changes and Response Mechanisms in a Karst Area of China from a Sustainable Perspective
Bibliographic record
Abstract
Eco-poverty alleviation policies have significant impacts on the changes in farmer household livelihoods. This study developed a multi-dimensional index system, which applies the social cognitive theory and farmer household livelihood capital to analyze the effects of eco-poverty alleviation policies on farmer household livelihood change in a karst area in China. The multivariate logistic, entropy weight, and Technique for Order of Preference by Similarity to Ideal Solution models were used to analyze the responses of 892 farmer households from eight villages in Guizhou Province, China. The results show that the Poverty Alleviation Resettlement Project (PARP) had the highest impact as it enables higher engagement of farmer households in non-agricultural activities, resulting in significant livelihood changes. Among the eco-poverty alleviation policies studied, changes in livelihoods of farmer households are highest from PARP, followed by the Ecological Forest Ranger Project (EFRP), Grain for Green Program (GGP), Forest Ecosystem Compensation Program (FECP), and Single Carbon Sink Program (SCSP). Specifically, GGP received the highest response from farmer households working out-of-province, whereas SCSP received the lowest. EFRP received the highest response from farmer households working in the village. Farmer households in different regions were found to respond differently to various eco-poverty alleviation policies, based on how specific policies can address their practical problems. It is also related to the delayed effects of these policies on their livelihoods. This study provides a theoretical basis for optimizing livelihood improvements for farmers at the regional level, which can aid in formulating strategies in the future to alleviate poverty and revitalize local rural communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".