Old Blue's Road: A Historian's Motorcycle Journeys in the American West
Bibliographic record
Abstract
In Old Blue's Road, historian James Whiteside shares accounts of his motorcycle adventures across the American West. He details the places he has seen, the people he has met, and the personal musings those encounters prompted on his unique journeys of discovery. In 2005, Whiteside bought a Harley Davidson Heritage Softail, christened it 'Old Blue,' and set off on a series of far-reaching motorcycle adventures. Over six years he traveled more than 15,000 miles. Part travelogue and part historical tour, this book takes the reader along for the ride. Whiteside's travels to the Pacific Northwest, Yellowstone, Dodge City, Santa Fe, Wounded Knee, and many other locales prompt consideration of myriad topics--the ongoing struggle between Indian and mainstream American culture, the meaning of community, the sustainability of the West's hydraulic society, the creation of the national parks system, the Mormon experience in Utah, the internment of Japanese Americans during World War II, and more. Delightfully funny and insightful, Old Blue's Road links the colorful history and vibrant present from Whiteside's unique vantage point, recognizing and reflecting on the processes of change that made the West what it is today. The book will interest the general reader and Western historian alike, leading to new appreciation for the complex ways in which the American West's past and present come together.--Provided by publisher.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".