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Record W4407590997 · doi:10.1007/978-3-031-71322-4_11

Infrastructural Containment and the Politics of Migration in the Mediterranean Sea

2025· book-chapter· en· W4407590997 on OpenAlexafffund
Lara Şarlak

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsUniversity of British Columbia
FundersInternational Development Research Centre
KeywordsContainment (computer programming)PoliticsMediterranean climateGeographyPolitical scienceArchaeologyComputer scienceLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.627
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.252
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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