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Record W4399083457 · doi:10.1080/08865655.2024.2356775

Maritime Sar Systems in the EU: Convergence and Co-optation into the Anti-Immigration Border

2024· article· en· W4399083457 on OpenAlexafffundvenue
Luna Vives, Arnaud Banos, Camille Martel, Elizabeth Rose Hessek, Kira Williams

Bibliographic record

VenueJournal of Borderlands Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsThe Scarborough HospitalUniversity of TorontoUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et Culture
KeywordsConvergence (economics)MilitarizationIrregular migrationMediterranean climatePolitical scienceImmigrationMediterranean seaGeographyEconomyExternalizationEconomic geographyDevelopment economicsInternational tradeRegional scienceEconomic growthBusinessEconomicsPolitics

Abstract

fetched live from OpenAlex

Research focusing on maritime Search and Rescue (SAR) operations in contexts of migration typically focuses on single-country studies, rarely engaging with the development of regional trends. This paper centers on five maritime areas along the southern EU border (Eastern Mediterranean, Central Mediterranean, Western Mediterranean, Canary Islands) and the Dover Strait to examine the transformation of maritime SAR in Europe over the last decade. Our analysis draws from secondary sources to understand trends in these five major sea routes to and out of the EU. We conclude that ways of doing maritime SAR in the region are converging and that the emerging approach to maritime SAR is defined by its co-optation into the anti-immigration border apparatus, its militarization and the externalization of SAR responsibilities to countries of origin and transit. This convergence of SAR policy is evident yet still incomplete and fragmented, with each country exhibiting distinct institutional arrangements.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.355
Teacher spread0.334 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations3
Published2024
Admission routes3
Has abstractyes

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