Studying Social Movements: Challenges and Opportunities for Participant Observation
Bibliographic record
Abstract
Abstract The research method of participant observation has long been used by scholars interested in the motivations, dynamics, tactics and strategies of social movements from a movement perspective. Despite participant observation being a common research method, there have been very few efforts to bring together this literature, which has often been spread across disciplines. This makes it difficult to identify the various challenges (and their interrelation) facing participant observers. Consequently, this article first reviews how participant observation roles have been conceptualised in general and then draws specific links to how the method has been used in the study of activism and social movements. In doing so, this article brings together key academic debates on participant observation, which have been considered separately, such as insider/outsider and overt/covert, but not previously been brought together.
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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 | 0.249 | 0.286 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".