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Record W4392162754 · doi:10.1080/1369183x.2024.2315350

Migration as a building bloc of middle-class nation-building? The growing rift between Germany’s centre-right and right-wing parties

2024· article· en· W4392162754 on OpenAlexaff
Oliver Schmidtke

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsRight wingMiddle classPolitical scienceWingLawPoliticsEngineering

Abstract

fetched live from OpenAlex

Over the past twenty-five years, Germany has seen substantial shifts towards more robust and expansive migration and integration policies addressing immigration primarily as a socio-economic resource and an irreversible reality defining contemporary German society. Yet, the idea of Germany as a ‘country of immigration' has remained contested and polarizing in electoral politics particularly on the political right: While the Christian Democratic Union (CDU) under Chancellor Merkel's leadership has gradually endorsed immigration as an integral part of its socio-economic modernization agenda, the far-right Alternative for Germany (AfD) has embarked on a nativist rejection of all forms of immigration and cultural diversity. Based on a frame analysis, the article argues that the idea of middle-class nation-building through immigration has allowed the Christian Democratic Union to move away from its traditional anti-immigrant stand and integrate related issues into its market-driven political agenda. The key hypothesis is that the Christian Democrats have been able to modernize the party’s middle-class nation-building ambition by adopting its basic rationale to the recruitment and integration of immigrants into German society. In contrast, the AfD has embarked on an opposing trajectory as the political advocate for identity-driven, anti-immigrant sentiments and an exclusionary nationalism.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.058
GPT teacher head0.357
Teacher spread0.299 · 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

Citations15
Published2024
Admission routes1
Has abstractyes

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