The impact of rural labor migration on forestry input allocation: evidence from Guizhou Province, China
Bibliographic record
Abstract
Rural labor migration, characterized by large-scale movement from agriculture to nonagricultural sectors, presents new challenges to sustainable forest management. Using survey data from 812 rural households in Guizhou Province, China, this study examines the impact of rural labor migration on household forestry input allocation and explores the underlying mechanisms. The findings indicate that labor migration significantly reduces forestry input, primarily due to a decrease in labor inputs. Mechanism analysis reveals that labor migration increases household income, which, in turn, reduces farmers’ reliance on forestland. However, there is no direct evidence suggesting that labor migration facilitates land resource consolidation, such as forestland transfer, which could further constrain forestry inputs. Heterogeneity analysis shows that the negative impact of labor migration on forestry input is more pronounced among households with lower levels of education, limited political capital, and those operating on smaller scales. This study provides valuable insights for policy formulation aimed at enhancing household incentives for forest management, increasing forestry input, and promoting sustainable forestry development.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".