Infrastructural Containment and the Politics of Migration in the Mediterranean Sea
Bibliographic record
Abstract
Abstract This chapter questions how European nation-states develop maritime border securitisation strategies that are contingent on the ontology and materiality of terrain on which those borders are established. To do so, it uses ‘infrastructures’ as an analytical tool and explores how oceanic materialities are part of the complexity of migration in/at the Mediterranean Sea, raising questions on how seascapes are transformed into sites of control and conflict. It introduces the concept infrastructural containment to address the spatio-material strategies deployed by nation-states to further strengthen borders and contain ‘clandestine’ maritime crossings. The chapter questions how containment of migrant mobilities is configured through the fluid, vast and turbulent nature of the ocean and how its often hostile terrain plays an active role in the production of border infrastructures by becoming an environmental boundary. It argues that an infrastructural approach to water allows for additional ways of understanding technologies of containment at the Mediterranean Sea and the challenges posed by the ocean’s wetness and fluidity resists also the EU’s attempts to establish rigid territorial boundaries. This chapter underscores the complex and evolving nature of maritime borders, prompting a critical reflection on oceanic governance, which often relies on land-centric approaches to territorialisation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".