The influence of forestland tenure security on rural labor migration—further discussion based on migration location
Bibliographic record
Abstract
Amid China’s comprehensive deepening of the new round of collective forest tenure reform, we integrate legal, actual, and perceived levels of tenure security into an analytical framework. Using survey data from 508 rural households in the Jiangxi’s collective forest area, we apply Tobit and IV-Tobit models to reveal the impact of multidimensional forestland tenure security on labor migration, distinguishing between outside-of-county and intra-county labor migration, with heterogeneity across forestland management scales. Our results show that ownership of forest rights certificates and positive evaluations of the “separation of three rights” policy have significant positive effects on labor migration. Additionally, we uncover nuanced effects of tenure security on migration patterns, with different dimensions and scales influencing intra-county versus outside-of-county migration. These findings suggest that policymakers should focus on enhancing legal clarity, facilitating actual tenure stability to encourage rational labor migration, and optimizing rural resource allocation in China’s collective forest areas. As the collective forest rights reform continues to advance broadly, the government should strengthen the oversight system for policy implication, intensify policy publicity efforts, prioritize the implementation of local forest reform policies, and enhance farmers’ awareness and understanding of forest rights policies to fully leverage the positive role of forest rights safeguards.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".