Bibliographic record
Abstract
In this paper, we examine the contribution of the social Web in the events that led to the fall of the regime of Ben Ali in January 14, 2011. We take an interest in cyberactivism during the revolution for thinking about the ways of writing and archiving of the collective memory of Tunisia. In addition, the role played by cyberactivists through censored social platforms and websites is especially surprising in the sequence of events leading to the collapse considering how strong was the repression. We study the tools, methods and procedures of the actions carried out by cyberactivists to bypass repression, highlighting how in social events the Web acted as a relay of information, a catalyst of contestation and, since January 15, 2011, an outlet of the trauma of Ben Ali's fall. We also investigate about how coordination between the Web and the street took place in the mobilization of the protest. Finally, we reflect on the changing role of online activists in post-revolutionary Tunisia and the need to redefine their roles, their speeches and their goals. The debate about reclaiming the public sphere, a watermark of our analysis, permits to observe how the reconstruction process is at work. The learning of democratic public debate takes place in a context charged with conflict, tension and disagreements of various kinds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".