The Art of Neighboring beyond the Nation: Ethnic and Religious Pluralism in Southwest China
Bibliographic record
Abstract
Northwest Yunnan is nested in the border areas of Tibet, Myanmar, and Southwest China. The religiously and ethnically diverse region has astonishingly seen a lack of “conflict”, as is often assumed in regions of ethnic and religious differences. This paper argues that there is an organic form of pluralism through frequent inter-ethnic and inter-religious marriages, multi-lingual daily interactions, and strategic ethnicity registrations. Ethnic and religious boundaries are made permanently or temporarily permeable through the celebration of boundary-crossing rituals such as weddings and funerals and other shared experiences such as collective labor and migrant work. Despite an increasingly strong push to be integrated into the state power through various top-down developmental projects, minority peoples here still use kinship, collective rituals, and other shared experiences to foster group formation that is fluid, porous, and malleable, instilling empathy and obligation as the basis of this pluralistic borderland society. This organic form of pluralism presents an alternative to the nation as the standard modern form of community. This paper ultimately argues that this specific type of plurality requires us to think beyond the normative liberal notions of religious tolerance and diversity that are still promoted within the frame of the exclusivist nation-state.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".