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Record W2744748914

Refugees and Open Borders: How sustainable is the Schengen?

2017· article· en· W2744748914 on OpenAlexaff
Mohammadhossein Asadi Lari

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Criminal Justice and Data Protection
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRefugeePolitical sciencePoliticsMember statesFrontierLaw and economicsOrder (exchange)Political economyEuropean unionLawSociologyInternational tradeBusiness
DOInot available

Abstract

fetched live from OpenAlex

The Schengen has been a prime example of European integration, providing citizens of member states the unique experience of travelling across borders without the inconvenience of border checks. However, the recent peak in the flow of refugees and a changing political environment has challenged the agreement and out the future of open borders in Europe into question. This article initially establishes the background on the Schengen and Dublin conventions, the benefits they have brought to member states and the role they have played in European integration. Subsequently, the challenges brought by refugees and the reactions of European nations is then discussed and finally the attempts by members to address the current challenges are assessed. In summary, I argue that the situation can only be resolved with political will from all member states in order to make the tough decisions required to maintain an achievement that was itself reached after a collective effort by all member states. There is also a need to realize the extra burden that frontier states are bearing and the need for sharing the responsibility of any collective decision. Inaction or counterproductive measures would either challenge the moral responsibility of the EU in protecting those risking their lives to reach its shores or result in the end of a borderless Schengen zone. Both scenarios are undesired, underscoring the importance of robust, collective action.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.812
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0030.001
Open science0.0010.001
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 head0.065
GPT teacher head0.399
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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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