Activism in the Middle East and North Africa in times of upheaval: social networks’ actions and interactions
Bibliographic record
Abstract
We seek to better understand recent changes in social mobilization in the MENA region by analysing the formation and evolution of social networks. We propose an interactive perspective linking up contentious politics with routine governance through a dynamic articulation of repertoires of contention. At the heart of our analysis of social networks lie important questions regarding agency, strategic action and outcomes that have significance for social mobilization, social movements and politics at large. We outline how mobilization can change suddenly in the face of dramatic social and political events that transform societal interactions and adopt a bottom-up approach that highlights how micro level interactions in times of crisis produce specific logics and dynamics inside networks and shape what the networks achieve. By starting with descriptions of interactions at the grassroots level, we seek to explain macro level dynamics between networks and other players, including the state. In our approach, the role of other players becomes as important as, if not more than, structural characteristics. By adopting an interactionist orientation, we reveal the temporal dimension of strategic and non-strategic choices of these different players. In this perspective, the internal dynamics of the networks play a crucial part in determining the strategy of mobilization at the time of unrest; they also shape the possibilities for reformulating of the identity of the movement. Equally, the interactions between networks and other social and political players during episodes of contention contribute to validate or invalidate the internal choices of the networks; they also shape the impact of the networks’ mobilization on the trajectory of the protests. Finally, the resonance of the networks with the behaviours and identities activated by the upheaval simultaneously empower them as players and tie their fate to a specific type of demands and needs which may be more or less transient.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".