Red stamps and green tea: fieldwork negotiations and dilemmas in the Sino‐<scp>V</scp>ietnamese borderlands
Bibliographic record
Abstract
The socialist spaces within the upland Southeast Asian Massif are home to over 70 million people belonging to geographically dispersed and politically fragmented ethnic minority populations. State authorities have long considered these upland margins as frontier regions where ‘inconsequential peoples’ lag behind national standards. Over time, the Chinese and Vietnamese states have worked to enclose these spaces through a range of ‘development’ programmes and politico‐economic strategies. Undertaking qualitative social science research here is underscored by a specific set of challenges (red stamps), dilemmas and negotiations (green tea). In a contemporary context that interweaves economic liberalisation with centralised and authoritarian political structures, I explore how I have negotiated and manoeuvred access to ethnic minority voices. Specifically, I focus on fieldwork endeavours in the Sino‐Vietnamese borderlands to answer two core questions. First, in these socialist arenas, how can researchers negotiate access to still‐marginalised groups misunderstood by the central state? And second, what are the most pressing ethical questions raised by cross‐cultural fieldwork in these spaces and how might these be addressed? While debating these ethical and methodological challenges, I reflect upon the numerous roles of gatekeepers, concerns over the well‐being of interviewees and the importance of self‐censorship.
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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 | 0.057 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.041 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".