Order from Chaos: How Networked Activists Self-Organize by Creating a Participation Architecture
Bibliographic record
Abstract
Collectives attempting to self-organize without relying on managerial control can leverage open, digital networks to foster information exchange and agility. But, as collectives grow, the open boundaries that enable the mobilization of participants and rapid exchange of ideas can give rise to new organizing challenges that make collective action untenable. We examine this tension by exploring how networked activists self-organize through open, digital networks to achieve shared aims without belonging to a common organization that supports their cause. With a seven-year, inductive field and archival study, we capture how activists from the Anonymous collective organized 70 protest actions while struggling to integrate newcomers and coordinate increasingly complex activities. Rather than succumb to chaos or managerial control, Anonymous learned to self-organize, gradually abandoning normative forms of control in favor of forms of architectural control. By creating a participation architecture—a sociotechnical framework that empowered technical experts and unobtrusively channeled newcomers to designated forums—networked activists enhanced their collective ability to coordinate complex, interdependent actions at scale. Our grounded theoretical model reveals how the challenges of self-organizing emerge with rapid growth and how these can be overcome by configuring architectural control.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".