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Record W2152318758 · doi:10.1177/002071520304400401

National Identity Issues in the New German Elites: A Study of German University Students

2003· article· en· W2152318758 on OpenAlexvenueno aff
Elizabeth D. Ezell, Martin Seeleib‐Kaiser, Edward A. Tiryakian

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsGermanNationalismNational identityContext (archaeology)Identity (music)Gender studiesSociologyElitePolitical scienceEuropean unionPoliticsLawGeography

Abstract

fetched live from OpenAlex

This empirical study treats German university students as a rising elite. After discussing the broader sociohistorical context of German national identity in recent decades, this study analyzes (a) students’ attitudes and perceptions on issues related to nationalism, national identity, and inclusion, and (b) the extent to which the “Wall in the Mind” as a psychological chasm persists in the new post-cold war generation of West and East Germans. Survey data were obtained from a sample of 544 students at 11 universities in the three areas of Germany: West Germany, East Germany, and Berlin. A major finding on the issue of national identity, as manifested in common symbols and common tasks, is a blurring of regional differences, with no significant differences found by age or gender. Overall, students reject “traditional” nationalism in favor of a “post-national” commitment to transnational values such as human rights and social equality for all. A significant majority feel there is a common German culture and that East and West Germans are forming one people; one whose future lies in being part of the European Union. We conclude there may be an emergent trend among university students of a post-national identity of being German-in-Europe, which warrants further comparative research.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.297
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.080
GPT teacher head0.502
Teacher spread0.422 · 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
Published2003
Admission routes1
Has abstractyes

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