Challenges and dilemmas: fieldwork with upland minorities in socialist Vietnam, Laos and southwest China
Bibliographic record
Abstract
The Chinese, Vietnamese and Lao spaces within the upland Southeast Asian massif, sheltering over 80 million people belonging to geographically dispersed and politically fragmented minority populations, have only recently reopened to overseas academic endeavours. Undertaking social sciences research there among ethnic minority groups is underscored by a specific set of challenges, dilemmas, and negotiations. This special issue brings together Western academics and post-fieldwork doctoral students from the realms of social anthropology and human geography, who have conducted in-depth fieldwork among ethnic minorities in upland southwest China, northern Vietnam, and southern Laos. The articles provide insights into the struggles and constraints they faced in the field, set against an understanding of the historical context of field research in these locales. In this unique context that nowadays interweaves economic liberalisation with centralised and authoritarian political structures, the authors explore how they have negotiated and manoeuvred access to ethnic minority voices in complex cultural configurations. The ethical challenges raised and methodological reflections offered will be insightful for others conducting fieldwork in the socialist margins of the Southeast Asian massif and beyond. This specific context is introduced here, followed by a critique of the literature on the core themes that contributors raise.
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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 | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.019 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".