The Medium of the Gezi Movement in Turkey: Viral Pictures as a Tool of Resistance
Bibliographic record
Abstract
This paper examines critically the role of the viral images spread through social media in the development of the Gezi movement in Turkey. On May 31, 2013, a small scale environmentalist protest for the preservation of a public park, which was planned to be converted into a shopping mall, transformed into a mass resistance against the authoritarian polices of the Islamist and neoliberal Justice and Development Party. This gathering of hundreds of thousands people over a very short time period in a moment when the mainstream media under government censorship did not cover the events, brought forward the question about the power of the viral pictures circulated through social media on the organization of a public resistance. From May 31st to June 15th, millions of tweets and Facebook posts on the Gezi Movement were shared in order to spread the news about the resistance. The horizontal and non-hierarchical way of distribution of the pictures through social media helped the formation of a common platform for the discussion among people from different social and political backgrounds and identities, and served to the construction of a pluralistic bloc for resistance. Through the experience of the Gezi Movement in Turkey and its dialogue with the ongoing uprisings in other parts of the world, I explore the power and limits of the viral pictures in the social media where individuals without a centralized leadership can come together online in order to construct a strong resistance offline.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".