Doing Ethnography on Social Media: A Methodological Reflection on the Study of Online Groups in China
Bibliographic record
Abstract
This article draws on the two authors’ extensive fieldwork experiences in studying Chinese feminists and lawyers on social media to offer some thoughts on how to conduct qualitative research in the digitalized world. We argue that qualitative methods such as participation observation, in-depth interview, and textual analysis can provide thick descriptions and deep, localized knowledge of social processes that go far beyond the sketches of Big Data. Social science data collection and analysis on social media need not only Big Data’s bird’s-eye view, but also the day-to-day ethnographic immersion—“living on the sites” and interacting with research subjects over a long period of time. The rise of social media has not changed the basic principles of doing ethnography, such as the importance of immersion and reflexivity. Nevertheless, ethnography of online groups presents new challenges and opportunities in terms of accessing field sites, analyzing ethnographic data, and research ethics.
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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 | 0.030 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".