Bibliographic record
Abstract
In some respects the story of Studebaker is the same as that of every car company except General Motors, Ford, and Chrysler. It produced some notable cars, was considered a leading firm at times, and survived longer than most. Yet Studebaker failed like nearly every other car maker. This book wants to explain why. Thomas E. Bonsall is the author of identification guides and popular histories of the Chrysler 300, Firebird, GTO, Mercury and Ed-sel, Avanti, Pontiac, and Lincoln. This volume is not a buff book, but neither is it a scholarly history. In a narrative, chronological structure, Bonsall tracks the company's rise and fall. The Studebaker brothers started as carriage builders in 1852, entered the auto industry as the maker of bodies for the Pope Company's electric taxis in the late 1890s, and began building automobiles after 1900. They grew slowly until the 1920s and then struggled to survive the Great Depression. Studebaker revived during the war and emerged with high hopes, which turned into a desperate struggle for existence for a decade after 1955. Studebaker ceased making cars in its original factory in South Bend, Indiana, in December 1963. An assembly line in Hamilton, Ontario, operated until March 1966, while non-auto activities continued as Studebaker-Worthington until 1978.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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.
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".