Social Media and Social Justice Activism
Bibliographic record
Abstract
In December 2010 a 26-year old unemployed Tunisian university graduate named Mohamed Bouazizi set himself on fire in protest after the fruits he was selling in the town of Sidi Bouzid were confiscated by government officials who alleged he was operating his stand without a license. Three weeks later he died in hospital, sparking massive street revolts by Tunisian citizens frustrated by government corruption and widespread unemployment. Tunisia’s repressive government intervened, imposing curfews, closing schools and universities, arresting citizens and violently setting the police onto the thousands of citizens who had taken to the street. Tunisia is infamous for its authoritarian media and internet system, decried by the International Federation of Journalists (IFJ) and its affiliate, the Syndicat national des journalistes tunisiens (SNJT) in their campaign for journalistic independence. \n\t \nInitially Western mainstream media ignored the Tunisian protests, partly because of a lack of official information from the government, but citizens, using various social media, were able to spread timely information about the protests to the world, and mobilize Tunisian citizens and the Tunisian diaspora, including a large community in Montreal. Information was disseminated via Facebook (even after the government deleted pages critical of the government), WikiLeaks, the Tunisian blog Nawaat which posted amateur videos online, proxy servers that could bypass government monitoring, and Twitter – which was also able to more easily circumvent government censorship (Al Jazeera English 2011). After 23 years of autocratic rule, President Ben Ali fled the country, and the country is now undergoing a shift in governance, which Tunisians hope will usher in a reign of democratic transparency. \n\t \nMany Western commentators dubbed the actions in Tunisia “The Twitter Revolution,” celebrating the use of social media for mobilizing Tunisians and toppling the Ben Ali government. But others were more cautious, attributing the actions of Tunisians to “decades of frustration, not in reaction to a WikiLeaks cable, a denial-of-service attack, or a Facebook update” (Zuckerman 2011). Jillian York, also hesitant to ascribe power to networked technologies, remarked that “I am glad that Tunisians were able to utilize social media to bring attention to their plight. But I will not dishonor the memory of Mohamed Bouazizi–or the 65 others that died on the streets for their cause–by dubbing this anything but a human revolution” (York 2011).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".