Gezi Assemblages: Embodied Encounters in the Making of an Alternative Space
Bibliographic record
Abstract
The article aspires to make a claim for the potential of the Deleuze-Guattarian concept of assemblage (agencement) to account for the Occupy Movements in general and 2013 Turkey Gezi Movement in particular. Throughout the article, it is claimed that the concept of agencement provides us with useful tools to elucidate the constitution of a new dissident community in Gezi Park and the subsequent park assemblies. Special emphasis will be put on the capacity of the concept to account for the embodied and embedded nature of the Gezi Movement, an argument further supported by data coming from participatory observations throughout different phases of the mobilization, 23 in depth interviews with activists from different political backgrounds and minutes of the park assemblies. Although the concept of assemblage has started to be used in the analysis of social movements (Bennett, 2005; Chesters & Welsh, 2006; Lockie 2004; McFarlane, 2009; Rodriguez-Giralt, 2011, 2015; Rodriguez-Giralt & Marrero-Guillamón,2018), not much emphasis is given to the concepts of embodiment and body in assemblages. This article aspires to contribute to the literature by first underlining the importance of embodiment in the Gezi movement and second elucidating it with respect to the concept of body in the original use of the term of assemblage by Deleuze and Guattari (1980).
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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".