Willingness and Influencing Factors of Farmers’ Forestland Management in Ethnic Minority Areas: Evidence from Southwest China
Bibliographic record
Abstract
This paper uses a questionnaire and interviews from households in ethnic minority areas of the Jianchuan County (Dali Bai Autonomous Prefecture) and Pingbian County (Honghe Hani and Yi Autonomous Prefecture) in Yunnan Province to explore the willingness of foresters to manage forests. Using the Sustainable Livelihoods Analysis framework, we select three indicators including the variables of individual social economic attributes, the cognition and experience of forest landowners, and policy guidance. We use a binary logistic regression model to analyze the factors affecting the willingness of foresters to participate in forest management. Through the above analysis, we found the following: (1) Forest landowners’ willingness to engage in forest management in ethnic minority regions is relatively high, at 71.98%. (2) Variables of individual social economic attributes have the most significant degree of influence on the willingness to engage in forest management. (3) Standard of living and the woodland area have a significant positive effect on forest land management intentions, while education level, whether they are compensated by public welfare forests, and whether they have participated in the project of returning farmland to forest and grassland have a significant negative effect on management intentions. (4) There are significant differences between forest landowners’ willingness to engage in forest management and the influencing factors between minority regions and non-minority regions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".