Dai in the “Land of Tropical Miasma”: Encounters of Early Chinese Anthropology in Yunnan
Bibliographic record
Abstract
In early- to mid-twentieth century China, the tropical landscapes and indigenous peoples of southern Yunnan entered public consciousness in two different modes of representation: as a desolate and unfamiliar frontier fraught with the peril of diseases and in desperate need of environmental and social engineering; or, as a haven of fertile land with an ideal of harmonious society. In the process of making new senses of this tropical border region, anthropology played a major role as Chinese anthropologists working in this newly institutionalized discipline turned the Dai, traditionally regarded by Han people as a marginal group living within a dangerous land of zhangqi (tropical miasma), into an ethnographic subject. From Ling Chunsheng’s vision of environmental modification and medical advancement as a twofold project to engineer a new landscape and a new people, to Tian Rukang’s cultural critique that imagined the way of life of Dai people as an antidote for modernity, this article examines early Chinese anthropological discourses on the Dai people and their lived environment. I investigate how technological and epistemological changes fundamentally reshaped the meaning of tropical landscapes in China, a multi-ethnic country of a vast and diverse territory struggling to rejuvenate within a new global order, and I ponder the symbolic and material consequences of this recent history.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.023 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".