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Record W3040508812 · doi:10.1080/08865655.2020.1787190

Resistance or Acceptance? The Voice of Local Cross-Border Organizations in Times of Re-Bordering

2020· article· en· W3040508812 on OpenAlexvenueno aff
Sara Svensson

Bibliographic record

VenueJournal of Borderlands Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
FundersEuropean University Institute
KeywordsRefugeeGermanResistance (ecology)NormativeEuropean unionArgumentation theoryPolitical scienceInclusion (mineral)Refugee crisisRefugee lawSubject (documents)Political economySociologyLawInternational tradeGender studiesEconomicsHistoryLinguistics

Abstract

fetched live from OpenAlex

National borders in Europe are increasingly subject to re-bordering processes, including the external and internal borders of the European Union. This article asks if and how local cross-border organizations (Euroregions) have reacted to to the recent hardening of these borders. The Austrian-German border is one where border controls have been re-introduced in the wake of the 2015 refugee crisis, and which also has significant local cross-border institutional activity. Based on an analysis of 350 written items, published by six Euroregions during the five-year period 2015–2019, the article finds that the Euroregions have generally not voiced resistance to this development and have not been active in relation to the policy field of refugee or migrant inclusion. When they reacted, the resistance has mainly been embedded in an argumentation linked to instrumental concerns, such as the traffic situation, even though the research also demonstrated the existence of normative arguments related to human rights discourses and rights of migrants.

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)0.015
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0060.018
Scholarly communication0.0140.010
Open science0.0010.008
Research integrity0.0030.004
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 head0.036
GPT teacher head0.418
Teacher spread0.383 · 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 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

Citations13
Published2020
Admission routes1
Has abstractyes

Explore more

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