Ethnographic Approaches to Contentious Politics: The What, How, and Why
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
How should we study contentious politics in an era rife with new forms of contention, both in the United States and abroad? The introduction to this special issue draws attention to one particularly crucial methodological tool in the study of contention: political ethnography. It showcases the ways in which ethnographic approaches can contribute to the study of contentious politics. Specifically, it argues that “what,” “how,” and “why” questions are central to the study of contention and that ethnographic methods are particularly well-suited to answering them. It also demonstrates how ethnographic methods push scholars to both expand the objects of inquiry and rethink what the relevant units of analysis might be. By uncovering hidden processes, exploring social meanings, and giving voice to unheard stories, ethnography and “ethnography-plus” approaches contribute to the study of contention and to comparative politics, writ large.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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