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Record W4247041174 · doi:10.22215/rera.v11i1.256

Citizenship Ceremonies in Germany: A More Universalist Kulturnation

2017· article· en· W4247041174 on OpenAlexvenueno aff
Maria Jakob

Bibliographic record

VenueReview of European and Russian Affairs · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicReligion, Theology, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsNaturalizationCitizenshipCultural assimilationPledgeAllegianceCivicsGermanSociologyCommodificationViewpointsSocial sciencePolitical scienceLawLinguisticsImmigrationEconomy

Abstract

fetched live from OpenAlex

After 2000, a profound liberalization of naturalization criteria in Germany was followed by culturalist setbacks: language requirements were tightened, a civics test, a pledge of allegiance, and citizenship ceremonies were introduced. Can these developments be termed a liberal assimilationist turn in what it means to be a citizen or is this a revival of the old notion of a German cultural identity as Kulturnation (cultural nation)? The qualitative study of German citizenship ceremonies presented herein, provides an in-depth analysis of the logics of “liberal assimilation” and the nexus between culture, citizenship, and national belonging. At the ceremonies, “culture” is referenced as a universally human feature that binds people together. Conversely, culture is also presented as an individual folkloristic asset that can be used for profit and for contributing to the value of diversity. This twofold conceptual specification of “culture” is then discussed as an adaptation of the originally universalist Kulturnation idea and as a modern solution to the problem of societal integration.

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)0.003
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.008
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.270
Teacher spread0.241 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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