Doing fieldwork the Chinese way: a returning researcher's insider/outsider status in her home town
Bibliographic record
Abstract
Insider/outsider status has been recognised in geographical literature as an important aspect of positionality on which researchers should reflect critically. Based on my fieldwork experience in Dali, southwest China, this paper articulates an account of the co‐existence of ‘insiderness’ and ‘outsiderness’ during the research process in a way that adds nuance to scholarly challenges to conceptions of insider/outsider status as an oppositional binary. I touch on several dilemmas that arose over the course of my fieldwork in my home town, such as working with local research assistants, ‘encountering’ a Western supervisor in the field and interviewing local people. I argue that interacting in the field with people from different ethnic, professional or socioeconomic characteristics dynamises a researcher's insider/outsider position, bringing his or her in‐between position to the fore. In this paper, I highlight the tensions and negotiations arising from my experience of in‐betweenness in Dali. I also point out several particularities of doing fieldwork in China by referring to Chinese ways of thinking and communication, and analyse how insiderness complicates the research process with particular regard to China. Finally, I conclude that working in the field is not only a process of data collection, but also a process of learning ‘who I am’.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.027 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".