Dai Identity in the Chinese Ecological Civilization: Negotiating Culture, Environment, and Development in Xishuangbanna, Southwest China
Bibliographic record
Abstract
The Ecological Civilization (Eco-Civilization) is a Chinese political framework to advance a renewed human–nature relationship that engenders a sustainable form of economic development, and its narratives provide political impetus to conserve ethnic minority cultures whose traditional practices are aligned with state-sanctioned efforts for environmental protection. This official rhetoric is important in Xishuangbanna, a prefecture in Yunnan province renowned for its lush tropical rainforests and Dai ethnic minority. This article explores the relationship between Dai cultural identity and the Chinese state in the context of environmental concerns and development goals. Historical analyses of ethnic policies and transformations of landscapes and livelihoods are presented alongside descriptions of contemporary efforts by Dai community members and the Chinese state to enact Eco-Civilization directives, and they illustrate paradoxical circumstances in which political rhetoric and practice are seemingly at odds with one another, yet often contradict in such ways so as to further the Chinese state agenda. Moreover, case studies demonstrate how new policies and sustainable development efforts have often perpetuated structures and ideologies of the Maoist era to reinforce inequalities between central state powers and already marginalized ethnic minorities. These dynamics warrant further consideration as the Chinese government continues to champion its leadership in environmental governance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".