Bibliographic record
Abstract
The angry Parisians that gathered on the ramparts of the Bastille on that fateful day of July 1789 were unlikely to own mobile phones; nor had they enjoyed many chances, in the weeks leading up to the big event, of being updated on developments by bloggers or twitters.Still, they made it there.And they made it in sufficient numbers, with sufficient unity and commitment, to start what can be safely regarded as a quite successful and impactful mobilization.Of course, had they been able to rely on contemporary technologies, they would have assembled more quickly, and probably in greater numbers; but so would have, presumably, the tanks and helicopters of good king Luis.Should we then conclude that technologies be irrelevant to the making of 'revolutions' (as well as, more broadly, of sustained challenges to powerholders)?Or -more specifically -that they made no difference to the evolution of the events that took, and are still taking, place in most North African and Middle East countries since January 2011?Of course not (for one thing, communication technology -in that case, printed media -actually DID play a major role even in the development of modern social movements: Tarrow 2011, ch.3).But perhaps we should remember that technologies do not make collective action.Men and women do.They do so, it goes without saying, within the context of their times.But this is not a purely -nor a mainly -technological context.It is at best a multidimensional context, in which technology interacts with other factors to shape patterns of collective action.Among those factor are certainly the relational settings in which protesters are embedded, and which are at the same time created or re-shaped by the unfolding of collective action (Diani and McAdam 2003).Media scholar Ramesh Srinivasan ( 2011) is very much along the same lines when he writes, in reference to the recent Egyptian uprisings: ''By fixating on technologies and the few youth that actively use them, we ignore a much more powerful narrative -the story of how synergies are created between classes to mobilize as a network without depending on social media.In Egypt, these networks may include family connections, neighborhoods, mosques, and historical institutions, such as the previously banned Muslim Brotherhood.New technologies hardly erode or overwhelm these classic models of communication and information sharing.''Srinivasan's perspective is a welcome warning against an overly technological view of the Arab spring.At the same time, it would be misleading to frame current discussions on the role of ICT in recent protests (or collective phenomena at large, e.g. the August 2011 riots in England) as a conventional polarization between technology 'skeptics' and 'enthusiasts'.1 I am grateful to Claudia Padovani for her insights on the role of social media in mobilization processes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.056 | 0.010 |
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".