MétaCan
Menu
Back to cohort
Record W2552462195 · doi:10.1111/sena.12165

Perceived Anti‐Germanism in Austria

2016· article· en· W2552462195 on OpenAlexaff
Julia Greth, Thomas Köllen

Bibliographic record

VenueStudies in Ethnicity and Nationalism · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGerman legal, social, and political studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsGermanAttributionNarrativeDiversity (politics)HierarchySocial psychologyComparabilityResentmentPsychologySociologyGender studiesPolitical scienceHistoryLinguisticsLawAnthropology

Abstract

fetched live from OpenAlex

Abstract Germans working in Austria are confronted with several subliminal resentments. Research on nationalism, racism, and diversity has overlooked this topic up to now, as Germans, as well as other Northern American or Western European citizens, are very rarely analysed as marginalized groups. Furthermore, the situation of migrants from geographically, linguistically, and culturally close countries, has received scant attention and been deemed of little importance up to now. The anti‐German sentiments in Austria are primarily based on the Austrian self‐perception of being non‐German as a constitutive element of ‘Austrian‐ness’. Related to that, negative attributions ascribed to Germans simultaneously mean a positive attribution of ‘Being Austrian’. Based on a content analysis of ten narrative interviews, conducted with Germans working in Austria, it appears that they permanently experience being categorized as ‘the Germans’, which leads to several types of exclusion, demotion, and also discrimination in the workplace. Differences in hierarchy level and perceived competition seem to moderate this effect. It appears that workplace superiors of Germans tend also to put an emphasis on positive stereotypes related with Germans, whereas Austrian colleagues on the same hierarchy level tend to performatively construct and reproduce ‘Being German’ as a deficit that they associate with negative stereotypes.

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)0.001
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 score0.759
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.124
GPT teacher head0.424
Teacher spread0.300 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueStudies in Ethnicity and NationalismSame topicGerman legal, social, and political studiesFrench-language works237,207