Bare life at the European borders. Entanglements of technology, society and nature
Bibliographic record
Abstract
The sophisticated technology deployed for border control and surveillance at the EU borders poses a sharp contrast to the main causes for the deaths occurring in border areas, mainly due to abandonment to the elements. However, it is precisely the technology deployed at the borders which paradoxically pushes illegalized travelers to zones of greater exposure to “nature.” The article explores from a cultural-sociological perspective the new configurations of technology, “culture,” and “nature” that are emerging through the EU border regime. Drawing on Agamben's category of bare life, it claims that illegalized travelers are displaced into a sphere of mere biological survival. A content and discourse analysis of the narratives attached to technological products developed for border surveillance and control reveals a symbolical construction of the illegalized traveler as contiguous to “nature” while technology is depicted as if deprived of agency. Technology and nature are often perceived as neutral, ahistorical, and value-free, but are always socially and historically constructed. This construction expresses a certain understanding of the border scenario and an underlying definition of what is “human.” The paradox between the humanitarian and the securitization paradigms is thus only apparent: both rely on the construction of the illegalized travelers as bare life. A global biopolitical schism along the boundary between “nature” and “culture”—analogous to what Latour called the “great divide” between “moderns” and “others”—is being reproduced but also disputed and negotiated along the EU borders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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".