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Record W192066320

The Changing Nature of Migration in the European Union: Social Identity and Cohesion in a Globalized World

2011· article· en· W192066320 on OpenAlexaff
Michael Johns

Bibliographic record

VenueSSRN Electronic Journal · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsLaurentian University
Fundersnot available
KeywordsEuropean unionCitizenshipPolitical scienceImmigrationPolitical economyIdentity (music)Development economicsCohesion (chemistry)Member stateFace (sociological concept)Member statesInternational tradeSociologyLawPoliticsEconomicsSocial science
DOInot available

Abstract

fetched live from OpenAlex

As countries such as the United States look to address questions of ethnic identity in the face of the changing nature of borders and migration, the states of the European Union are facing even greater change. With the expansion east and the free movement of people throughout the Union, the EU is now faced with a unique and challenging problem. Mass migration of East Europeans into Western Europe is changing the receiving state’s society and can potentially lead to conflict in the future. One needs only look at the changing face of Dublin in the wake of large-scale migration of Poles and other East Europeans to see how a society must quickly adapt to a new understanding of citizenship and identity. Moreover, as these migrants are arriving from European Union member states, they have established EU citizenship rights which limit how countries such as Ireland can control their own immigration policies. This paper will examine how this change in the nature of migration is affecting not only the receiving states of the EU but also the states from where the migrants came and the EU generally. This analysis, relying on both primary interviews and secondary sources, will shine a light on the concept of social cohesion in a globalized world and will provide a means of comparison for other states attempting to cope with similar issues of identity and transnational 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 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)0.012
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.293
Teacher spread0.275 · 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 designTheoretical or conceptual
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
Published2011
Admission routes1
Has abstractyes

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