Collective Identity, Organization, and Public Reaction in Protests: A Qualitative Case Study of Hong Kong and Taiwan
Bibliographic record
Abstract
Mainstream structuralist and new social movement theoretical approaches to studying social movements in Western sociological traditions fail to explain why the Sunflower movement fostered solidarity among the Taiwanese while Occupy Central caused public division in Hong Kong. In response, I argue that the successes and failures of both were a function of the consolidation and division of collective identity. Using a qualitative case study, this article analyzes the discursive constructions of collective identity as they intersect with protest spaces, drawing out the events in their protest cycles and identifying the mechanisms within them that constructed and deconstructed collective identity. In doing so, I illustrate three phases of collective identity construction: the creation of collective claims, recruitment strategies, and expressive decision-making. Ultimately, this explicates the movements’ differing outcomes, and how their decline both narrowed and broadened identity in ways that provide a repertoire of ideological narratives usable as recruitment strategies in future mobilizations.
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How this classification was reachedexpand
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".