Collective Action Dilemma after China’s Forest Tenure Reform: Operationalizing Forest Devolution in a Rapidly Changing Society
Bibliographic record
Abstract
Devolution is a promising tool to enhance forest management. The literature has discussed many factors that affect the outcomes of forest devolution policies; however, insufficient attention has been paid to the role of exogenous socio-economic changes. Using the longitudinal case study method, we focus on how socio-economic changes affect the effectiveness of forest devolution policies using a case from Southeast China. We find that in this case, although forest devolution succeeded in granting farmers sufficient forest rights, it failed to incentivize farmers to contribute to managing forests because of the dramatic changes in socio-economic contexts. Economic development and outmigration reduced farmers’ dependence on forest income, elevated the costs of silvicultural operations, and posed market risks, thereby reducing farmers’ enthusiasm about managing forests; outmigration also weakened community leadership and impeded the collective action of making forest investments. Eventually, socio-economic changes compromised the positive stimulus caused by forest devolution and contributed to the collective action dilemma of managing forests after the reform. We argue that operationalizing forest devolution in developing countries needs to consider the exogenous socio-economic changes that may enhance or counteract the effects of devolution policies, and that more autonomy should be granted to communities to make policies adaptative to their local socio-economic dynamics.
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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 | 0.011 | 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 | 0.009 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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".