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Record W2625788814 · doi:10.1093/ahr/122.3.948

Matt Bera. Lobbying Hitler: Industrial Associations between Democracy and Dictatorship.

2017· article· en· W2625788814 on OpenAlexaff
Talbot Imlay

Bibliographic record

VenueThe American Historical Review · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsScholarshipDictatorshipGermanHistoriographyNazi GermanyContext (archaeology)NazismDemocracyPolitical scienceLawPolitical economySociologyEconomic historyHistoryPoliticsArchaeology

Abstract

fetched live from OpenAlex

The wave of scholarship over the last three decades on German business history during the Nazi regime provides the historiographical context for Matt Bera’s stimulating study Lobbying Hitler: Industrial Associations between Democracy and Dictatorship. In recent years, a key question in this scholarship has concerned the room for maneuver of German businesses. Whereas Peter Hayes and others highlight the increasing constraints under which industrialists operated, other scholars—most notably the late Christoph Buchheim, the inspirer of what has been labeled the Mannheim School—argue that companies in general continued to enjoy considerable freedom in determining the terms of their collaboration with the regime. Indeed, the latter present Nazi Germany’s economy as a variant of a capitalist economy in which the state influenced and even manipulated but did not replace the play of market forces. As Bera rightly notes, much of this scholarship focuses on individual companies, and hence the novelty and interest of his approach, which is to examine the history of two industrial organizations: the Association of Iron and Steel Industries (Verein deutscher Eisen- und Stahlindustrieller [VDESI]), led by Jakob Reichert, and the German Machine Builders’ Association (Verein Deutscher Maschinenbau-Anstalten [VDMA]), led by Karl Lange.

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)0.001
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.835
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0090.001
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 head0.105
GPT teacher head0.294
Teacher spread0.189 · 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
GenreCommentary

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
Published2017
Admission routes1
Has abstractyes

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