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Record W1846391147 · doi:10.18740/s46g6t

Towards a Historical Materialist Approach to Racism in Post-'Unification' Germany

2008· article· en· W1846391147 on OpenAlexaffvenue
Juliane Edler

Bibliographic record

VenueSocialist studies · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean history and politics
Canadian institutionsYork University
Fundersnot available
KeywordsUnificationIdeologyGermanHegemonyMaterialismRacismSociologyPoliticsGender studiesCitizenshipPolitical economyPolitical scienceLawEpistemologyHistoryPhilosophy

Abstract

fetched live from OpenAlex

This paper problematizes the subtext of ‘race’, which underpinned the contradictory process of German ‘unification’. The following question guides my inquiry: how and why have ‘white’ East German workers in post-‘unification’ Germany come to think of their ‘Germanness’/’whiteness’ as meaningful? Clearly drawing from the work of David R. Roediger, I argue that ‘white’ East German workers were paid the ‘wages of Germanness’. The concept is fleshed out as I interrogate three interrelated dimensions of changes pertaining to the lived experiences of (‘white’) East German workers: (1) German citizenship regulations with its lines of inclusion and exclusion; (2) the qualifier East denoting the existence of various degrees of Germanness; (3) individualized market dependence giving rise to conflicted emotions. Setting in motion a process of extensive and complex change, ‘unification’ had an impact on social relations of power, lived experiences and cultural means. The concept ‘wages of Germanness’ expresses the connections between political, ideological and economic aspects of ‘unification’, and further brings into focus the historical legacy of racialized notions of Germanness. Using the framework of historical materialism, this paper articulates a critique of hegemonic ideology, which suggests that racism in post-‘unification’ Germany was, by and large, spatially confined to <em>East </em>Germany.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.795
Threshold uncertainty score1.000

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 studies0.0010.001
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 head0.123
GPT teacher head0.351
Teacher spread0.228 · 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.

Study designNot applicable
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
Published2008
Admission routes2
Has abstractyes

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