The Ecology of Activism: Professional Mobilization as a Spatial Process
Bibliographic record
Abstract
This article develops an ecological theory that shifts the paradigm of professional mobilization from causes to relational spaces. It analyzes different species of activist professionals by locating them in an ecology of activism and examining how collective action emerges from their boundary work with the ecology's increasing density and consolidation. It empirically grounds the theory by explaining the political activism of Chinese lawyers in the early twenty-first century and how it led to a government crackdown in 2015. Using interviews, online ethnography, and archival data collected from 2005 to 2017, the research demonstrates that Chinese lawyers' political mobilization has experienced three stages: (1) vacancy and isolation (2000-2007), (2) spatial consolidation (2008-2011), and (3) boundary work (2011-2015). The study has implications for theories of social space and for understanding professional mobilization in authoritarian contexts and beyond.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".