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Record W2401920298 · doi:10.24043/isj.291

Managing migration, scaling sovereignty on islands

2014· article· en· W2401920298 on OpenAlexvenueno aff
Jenna M. Loyd, Alison Mountz

Bibliographic record

VenueIsland Studies Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsSovereigntyArgument (complex analysis)Political economyState (computer science)PoliticsHuman rightsCorporate governancePolitical scienceScholarshipSociologyLawEconomics

Abstract

fetched live from OpenAlex

Island and maritime spaces between regions have become central places of recurrent crises over human migration and re-articulations of state sovereignty. Islands, the very sites where land meets water, are among the contested sites of struggle over entry and exclusion. In this paper, the Mediterranean is our main area of geographical inquiry. We explore the connections between crises of sovereignty, migration and islands, seeking to enhance connections between scholarship on migration and sovereignty. We argue that migration management and its geographical articulation on islands involve persistent reconfigurations of sovereignty, particularly evident during times of crisis over human migration. Such crises and re-articulations of sovereignty are creative uses of geography that repeatedly lead to a failure to protect human rights. To develop this argument, we bring feminist theorists of state sovereignty into conversation with political geographers. We move across scales of governance and political mobilization to show how a reconfiguration of sovereignty through regional and national management regimes leads to complex legal geographies and sovereign entanglements that migrants and advocates must navigate to claim rights.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.010
Scholarly communication0.0060.006
Open science0.0010.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.316
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations42
Published2014
Admission routes1
Has abstractyes

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