Ecological Governance of Rubber in Xishuangbanna, China
Bibliographic record
Abstract
In recent years, the Xishuangbanna Tropical Botanic Garden (XTBG) and the Xishuangbanna prefecture government have woken up to the environmental threat that extensive monoculture rubber cultivation poses in this tropical site in southern Yunnan. In response, XTBG has become a centre of knowledge production on how to restore some ethnic minority farmers' rubber fields to natural forest. As is common globally, the plan relies on mainstream ecology and leaves out farmers' experience and opinions. Curiously, in the 1980s and 1990s XTBG was a centre of human ecology research on farmers' knowledge about managing ecosystems and protecting biodiversity. This paper traces the transformation in the values, actors, institutional configurations, and epistemic communities that enabled farmers' experiences to be central to knowledge production and inscription across surrounding landscapes in the 1980s and 1990s, only to be disparaged and ignored in the production of knowledge and landscapes in the 2000s. Farmers who once taught XTBG scientists the names of species and how to survive on them during the Cultural Revolution are now excluded from determining how to restore those same species to the hills around them. Our analysis suggests possible pathways for ecological knowledge to be generated as well as lost.
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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 | 0.000 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".