Bridging Women Rights Networks
Bibliographic record
Abstract
In recent mass protests such as the Arab Spring and Occupy movements, protesters used social media to spread awareness, coordinate, and mobilize support. Social media-assisted collective action has attracted much attention from journalists, political observers, and researchers of various disciplines. In this article, the authors study transnational online collective action through the lens of inter-network cooperation. The authors analyze interaction and support between the women's rights networks of two online collective actions: ‘Women to Drive' (primarily Saudi Arabia) and ‘Sexual Harassment' (global). Methodologies used include: extracting each collective action's social network from blogs authored by female Muslim bloggers (23 countries), mapping interactions among network actors, and conducting sentiment analysis on observed interactions to provide a better understanding of inter-network support. The authors examine these two distinct but overlapped networks of collective actions and discover that brokering and bridging processes can facilitate the diffusion of information, coalition formation, and the expansion of the networks. The broader goal of the study is to examine the dynamics between interconnected collective actions. This research contributes to understanding the mobilization of social movements in digital activism and the role of cooperative networks in online collective action.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".