“I’ll be back”: the emergence of recentralized forest devolution in the southern provinces of China
Bibliographic record
Abstract
Although forest devolution, as a type of decentralization, is a high priority in the policy agendas of developing countries, recentralization has also occurred. In this paper, we focus on emerging recentralization within the devolution process of Collective Forest Tenure Reform (CFTR) in China’s southern provinces and conceptualize this process as “recentralized forest devolution.” In this paper, we update a key framework for analyzing decentralization and recentralization in governance processes based on the “policy arrangement approach.” Case studies were conducted in four counties of the Fujian and Yunnan provinces by tracing governance dynamics from 2001 to 2019. Our study found that the central government has tightened upward accountability and recentralized power for environmental conservation since 2012 under the discourse of “Ecological Civilization.” At the local level, recentralized forest devolution was expressed in terms of the restricted timber harvest levels for the purposes of environmental conservation. Therefore, forest devolution could be more vulnerable than expected by researchers and potentially interwoven with recentralization processes. Discourses, actors, property rights, and power are, therefore, considered to be interwoven in the complex dynamics of decentralization and recentralization.
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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 | 0.002 | 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 | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".