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Record W3081345253 · doi:10.18357/bigr12202019602

The Schengen Crisis and the EU’s Internal and External Borders:

2020· article· en· W3081345253 on OpenAlexvenueno aff
Frédérique Berrod

Bibliographic record

VenueBorders in Globalization Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsEuropean unionMember statesPolitical scienceTreatyAppropriationInternational tradeTreaty of LisbonLawPolitical economySociologyEconomics

Abstract

fetched live from OpenAlex

The EU was founded on the project of “Europe without borders”, which means elimination of internal borders between Member States according to Article 26 of the Treaty on the Functioning of the European Union. The counterpart of this objective has been the transfer of the controls to the external EU borders. In the Schengen area, external borders are controlled by common principles and procedures encompassed in the 2016 Schengen Borders Code. Member States have negotiated the Schengen agreement to maintain such external border controls, with the aim of protecting their citizens from various dangers and guaranteeing their national migration policies towards third-country nationals. Member States have therefore transposed the function of national border controls to the external EU borders. Cross-border cooperation within the EU has developed to reinforce the Schengen Space of free movement and has been jeopardized by the unorganized massive peak arrivals of migrants in 2015. This article analyses whether the 2015 Schengen crisis confirms the security-orientated approach or not, specifically as the crisis confronts the EU with national claims to recover the control of internal borders. It has been argued that this movement is proof of the resilience of Westphalian borders. This article is an attempt to show how European judicial power tried to limit such a national re-appropriation of borders, leading to a functional distinction between internal and external borders that may allow a departure from an exclusive security-orientated approach of external borders of the European Union towards a more cohesive approach to controls at EU external borders.

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)0.001
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.011
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.011
Scholarly communication0.0080.007
Open science0.0000.005
Research integrity0.0030.003
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 head0.022
GPT teacher head0.342
Teacher spread0.320 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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