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Record W2088496388 · doi:10.1080/15562948.2014.909076

Small States and Nonmaterial Power: Creating Crises and Shaping Migration Policies in Malta, Cyprus, and the European Union

2014· article· en· W2088496388 on OpenAlexaff
Ċetta Mainwaring

Bibliographic record

VenueJournal of Immigrant & Refugee Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsBalsillie School of International AffairsUniversity of Waterloo
FundersEuropean Commission
KeywordsEuropean unionPower (physics)Political scienceEconomyGeographyEnvironmental protectionPolitical economyInternational tradeBusinessSociologyEconomics

Abstract

fetched live from OpenAlex

This article examines how the power relationships between Malta and the Republic of Cyprus, on the one hand, and the European Union, on the other, shape irregular immigration policies in these two sovereign outpost island states in the Mediterranean. As member states on the EU's southern periphery, Malta and Cyprus have faced new institutional structures since their accession in 2004 within which they now construct their migration policies. Here, I examine how the new structures influence the discourse and logic of migration policies and politics and also how the seemingly small and powerless states affect regional policies. My contention is that, within this EU framework and with limited material power, the two outpost states have developed strategies based on nonmaterial power in order to defend and promote their interests. Such strategies have resulted in treating irregular immigration as a crisis in order to attract support. The new dynamics have thus resulted in more barriers to migration, and in negative consequences for the individual migrants and refugees on the islands. Although the strategies of Malta and Cyprus have been surprisingly successful in influencing regional migration governance, their long-term effectiveness is questionable, and their effects on the migrant and local population problematic.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.026
GPT teacher head0.287
Teacher spread0.261 · 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

Citations39
Published2014
Admission routes1
Has abstractyes

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