Making Sense of Social Change: Observing Collective Action in Networked Cultures
Bibliographic record
Abstract
Abstract This article presents an overview of rising trends in the study of networked interactions conveyed by social media technologies and the emergence of new meanings associated with social change. In recent years, a healthy amount of studies has focused on ICT uses within collective action, considering social media tools to have become crucial components of many transnational movements and social change projects. Crossing boundaries between social movements theories, political science, and communication studies, literature suggests that ‘online activism’ and increasingly networked interactions may have transformed the meanings and definitions associated with ‘collective action’ and ‘social change’. To make sense of these meanings, we identify three approaches used by scholars, which focus on (i) the actual networking of actors, (ii) the diffusion of new repertoires and frames through networks, and (iii) making sense of new meanings conveyed within networked cultures . We conclude by suggesting the need for more comprehensive research to better observe and make sense of how's actors define collective action and how they use social media tools when striving to convey social change.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".