Doing Ethnography on Social Media: A Methodological Reflection on the Study of Online Groups in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it