The Biopolitical Border in Practice: Surveillance and Death at the Greece-Turkey Borderzones
Bibliographic record
Abstract
This paper examines biopolitical control practices at the Greece—Turkey borders and addresses current debates in the study of borders and biopolitics. The Greek and Frontex authorities have established diverse surveillance mechanisms to control the borderzone space and to monitor, intercept, apprehend, and push back migrants or to block their passage. The location of contemporary borders has been much debated in the literature. This paper provides a nuanced understanding of borders by demonstrating that while borders are diffusing beyond and inside state territories, their practices and effects are concentrated at the edges of state territories—ie, borderzones. Borderzones are biopolitical spaces in which surveillance is most intense and migrants suffer the direct threat of injury and death. Applying biopolitics in the context of borderzones also prompts us to revisit the concept. While Foucault posits that biopolitics is the product of the historical transition away from sovereign powers controlling territory and imposing practices of death towards governmental powers managing population mainly through pastoral, productive, and deterritorialized techniques, the case of the Greece—Turkey borderzones demonstrates that biopolitics operates through sovereign territorial controls and surveillance, practices of death and exclusion, and suspension of rights. This study also highlights the fact that, despite the biopolitical realities, migrants continue to cross the borders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".