Analysis on the Willingness of Peasant Households for Forestland Use Right Transfer in the Background of Collective Forest Tenure Reform: A Case Study in Guangyuan City in Sichuan Province
Bibliographic record
Abstract
The objective of this study is to get a better understanding and accurate information regarding factors affecting the forestland transfer, providing first-hand information, and proposing policy implications. The forestland use transfer is the main content in the collective forest tenure reform in China at present. Individual household, as a basic unit of forest production, should be the major participant in the forestland transfer. Using survey data of 329 rural household in 18 villages of three counties, this paper analyzes influencing factors on rural householders’ willingness to partake in forestland use right transfer by employing Binary Logit Regression. Nineteen variables, which were identified as characteristics of householders and households, natural endowment of forestland resources and householders’s awareness of forestland policy. Results indicate that householders’ awareness of pertinent policies and population of the household demonstrate significantly positive effects, while forestland area owned by individual household shows a markedly negative effect on peasants’ willingness to participate in forestland transfer. Some policy implications are discussed in this paper.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".