Matt Bera. Lobbying Hitler: Industrial Associations between Democracy and Dictatorship.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".