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Record W2143688833 · doi:10.1017/s1479244314000638

PROVINCIALIZING AMERICA: NEW AND NOT SO NEW INTELLECTUAL HISTORIES OF WEIMAR GERMANY

2014· article· en· W2143688833 on OpenAlexaboutno aff
Rüdiger Graf

Bibliographic record

VenueModern Intellectual History · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean history and politics
Canadian institutionsnot available
Fundersnot available
KeywordsWeimar RepublicFlourishingGermanIntellectual historyPoliticsHistoryField (mathematics)ClassicsPolitical scienceSociologySocial scienceArt historyEconomic historyLaw

Abstract

fetched live from OpenAlex

Together the two volumes under review contain over forty essays on the intellectual history of Weimar Germany and its legacy today. The wide interdisciplinary field of authors, historians, philosophers, theologians, and literary, legal, and religious scholars, as well as social and political scientists, testifies to the continuing fascination of this era of thought in Anglo-American academia. With the exceptions of Mitchell G. Ash, Michael Krois, and Klaus Tanner, the authors teach at American, British or Canadian universities and represent major tendencies of the anglophone engagement with Weimar's intellectual history. Despite the fact that intellectual history of the Weimar Republic has been a flourishing field of research in Germany over the last decades, the volumes contain no contributions by German historians. This observation is by no means negligible in an age of transnational academic exchange, as may be exemplified by the recentOxford Handbook of Modern German History, which contains contributions by German, American, and British experts in their fields.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.068
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0040.003
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.257
Teacher spread0.213 · 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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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