Fieldwork among the Dong national minority in Guizhou, China: Practicalities, obstacles and challenges
Bibliographic record
Abstract
Abstract The People's Republic of China (PRC) is increasingly open to foreigners undertaking social science fieldwork; yet obstacles remain. Working with ethnic minorities adds further complexities because of the sensitive topics such research may raise. Based on recent fieldwork among the Dong in southeast Guizhou, as the first foreign researcher to ask for and gain official permission to work in the region, this article exposes some of the challenges, both practical and methodological, of conducting research in the PRC. Gaining access to my field site was a long trek through the hierarchic maze of Chinese administration. While reflecting upon this process, I detail my negotiations with local authorities. I then examine how I found reliable statistical data, was able to access the voices of peasants, acted to protect the anonymity of dissident informants, and negotiated working with local research assistants once in the field. These aspects, in turn, highlighted the importance of considering positionality in the field. Although each person's experiences and routes to fieldwork are unique, there are recurrent issues that shape the research process in the PRC. I reflect upon a number of these here, in the hope that this can smooth the way for future researchers.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".