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Record W7000334848

Everyday life in Nazi Germany

2009· article· en· W7000334848 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsnot available
Fundersnot available
KeywordsGermanEveryday lifeNazismNazi GermanyHonourFraming (construction)Scholarship
DOInot available

Abstract

fetched live from OpenAlex

At a conference in 2007 in his honour at the University of Michigan, Alf Lüdtke commented that there are many different ways to research the history of everyday life: its practitioners are still more united by the questions they ask than by how they seek their answers. Its pluralism, and its marginality, has allowed Alltagsgeschichte to serve as a conduit for epistemological innovation into modern German history from other fields, such as the linguistic, postmodern, cultural and spatial turns. Yet the characteristically eigensinnig lack of consensus can make it challenging for individual scholars to explain precisely what they mean by Alltagsgeschichte to their readers. In this issue, German History is pleased to bring together an international panel of distinguished historians who are either practitioners of Alltagsgeschichte or whose scholarship has been significantly influenced by it: Elissa Mailänder Koslov (Kulturwissenschaftliches Institut Essen), Gideon Reuveni (University of Melbourne), Paul Steege (Villanova University), and Dennis Sweeney (University of Alberta). Since the 1960s, historians of everyday life have investigated many different periods of German history, but none so much as the Nazi era. It seems fitting then to focus this Forum on the brown elephant in the room. The contributors have been asked to think about how the history of everyday life has altered our interpretations of the Third Reich in particular and modern German history more broadly. But rather than starting with a framing question from the moderator, we begin instead with the sceptical doubts of a panellist. It would hardly be a Forum on Alltagsgeschichte without unruly acts of reappropriation. Andrew Stuart Bergerson (University of Missouri–Kansas City) moderates the discussion.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0730.002

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 head0.026
GPT teacher head0.211
Teacher spread0.185 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2009
Admission routes1
Has abstractyes

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