Bibliographic record
Abstract
Frank Steinbeck’s book is a meticulously researched account of certain aspects of German motorcycle history in the twentieth century. Before World War Two, Germany was a nation of motorcyclists, a country where per capita ownership of motorcycles was higher than anywhere else in the world and where car ownership lagged far behind the USA, Great Britain and France. In 1938, Germans owned one half of all motorcycles worldwide but only 4% of all automobiles (p. 9). The premise of this revised PhD dissertation about Germany’s ‘special path’ towards a motorized society—that it was made on a seat or sidecar rather than behind a steering wheel—is not new. Rudy Koshar, a noted historian of German automobility, though one Steinbeck overlooks, has highlighted dramatic differences in historical motorization trends, and Sasha Disko emphasized the special role of the motorcycle in Germany’s ‘deviant path’ (her term) in a 2008 dissertation. Yet Steinbeck definitely adds further detail to the basic picture. Using a wide array of primary sources drawn from federal, state, company (Daimler, for example) and driving club archives, alongside contemporary newspapers, motorcycle journals and magazines, he gathers together important elements of what it meant to produce, purchase, license, insure, park and repair a motorcycle in Germany during the last century.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".