The Construction of a New Sociality through Social Media: The Case of the Gezi Uprising in Turkey
Bibliographic record
Abstract
Abstract During Turkey’s Gezi Park Protests in the summer of 2013, millions of people became connected as fellow protesters. In the early days of the Gezi movement, the increase in participatory activism through social media made visible the police brutality exercised in the last days of May 2013 against a small group of environmentalists who were protecting Gezi Park from being demolished in order to build a shopping mall. Throughout Turkey’s political history, there has been no other example of this kind of spontaneous mass movement resisting the state apparatus with the large participation of diverse groups and self-convened protesters, without any dominant ideological appeal or leader affiliation. In this article, I will analyze the ways in which these patterns of contradictory interactions formed, evaluated, or triggered various types of social relationships, by critically examining the content of viral images, memes, and widely shared posts by Gezi protesters on social media. In the absence of internal cohesion or an ideological and organizational agenda, I argue that widely shared viral images, memes, and text messages provided the content to collaboratively construct and publicly frame the autonomous logic of the “Gezi spirit” by the Gezi protesters. I aim to analyze this new understanding of collective identity in autonomous logic processed through social media as a being-with (mit-sein), rather than a fusion of the individual to an enigmatic we-ness in order to represent “I”. I claim that this autonomous collectivity is driven by fluidarity as a public experience of the self in relation to the other without intermediary apparatuses and hence can be conceptualized as having built a new sociality.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.028 | 0.021 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".