The Politics of Emotions, Bio-Political Border Practices and the Question of (In)security on the Turkish Political Territorial Borders
Bibliographic record
Abstract
This paper conceptually and analytically delineates the operation and employment of discourses and the political emotions of fear and anxiety in the making, conception, and cartographic imagination of the contours of modern Turkey’s political territorial boundaries. This study posits that the emergence and formation of the Turkish political territorial borders after the traumatic and violent experience of the cataclysmic shrinking, collapse and disintegration of the Ottoman Empire continue to influence discourses, emotions, practices and policies of border security and policing. Borders and borderlands are not only sites where the state performs, exercises and displays its sovereign will and power in protecting national security, dignity, pride and honor, but also sources and harbingers of fears, anxieties and ontological (in)securities. In the context of the highly publicized immigration influx of Syrians, Iraqis, Afghanis and others into Turkey, this paper argues that discourses and emotions of fear, anxiety and hate regained new currency and have become consequential in the state’s eliciting of consent from the masses in instituting border walls, fences and harsh border policing practices and policies on the borderlands and in cities across the country.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".