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Record W4402804226 · doi:10.1080/08865655.2024.2394050

The Fences of Melilla and Evros: Assessing Logics of Opposition and Support from A Multiscalar Perspective

2024· article· en· W4402804226 on OpenAlexvenueno aff
Irene Cabrera, Alejandro Daly, Sara Rodríguez, Alejandra Montañez

Bibliographic record

VenueJournal of Borderlands Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsnot available
Fundersnot available
KeywordsOpposition (politics)Perspective (graphical)Economic geographyPolitical scienceGeographyComputer scienceLaw

Abstract

fetched live from OpenAlex

A rigorous understanding of the logic that support the transformation of European walls and fences requires a detailed study of the particular and interlinked visions of multiple actors beyond central states. This paper follows a multiscalar perspective to evaluate the logic of opposition to support from different actors regarding the border barriers between Melilla (Morocco-Spain) and Evros (Greece-Türkiye). It examines how neighboring states of Morocco and Türkiye, local governments, the European Union, and Amnesty International converge or not in their position towards each fence, considering their vision on specific dimensions of analysis, such as the purpose, suitability, and features of each fence. This paper adopts a qualitative methodology based on the compilation and analysis of discourses and official communications, local and international news media from 2013 to 2023, civil society reports, and scholarly literature. The article demonstrates more opposition to the Evros fence than the Melilla fence. Moreover, while various actors validate the purpose behind these artificial borders in reducing irregular migration, some actors show concerns about the effectiveness of this tool, and almost all stakeholders question the features of these fences. Notwithstanding this criticism, Spain and Greece have been able to advance in strengthening their fences.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.041
GPT teacher head0.398
Teacher spread0.357 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

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