From Paris to Peoria: How European Piano Virtuosos Brought Classical Music to the American Heartland
Bibliographic record
Abstract
Grand Tours is a chronicle of the American visits of five charismatic pianists-Leopold de Meyer, Henri Herz, Sigismund Thalberg, Anton Rubenstein, and Hans von Bulow-during the late nineteenth century. Performing Beethoven and Chopin in gold-rush era California, these pianists introduced many Americans to the delights of the concert hall. With humor and insight, Lott describes the clash between the flamboyant, elegant, European pianists and American audiences more accustomed to circuses and rodeos than these entertainments. Lott also explores the creative and sometimes outlandish publicity techniques of managers seeking to capitalize on rich but uncharted American markets. The tours, which included almost a thousand concerts in more than one hundred cities in America and Canada, illustrate the rigors of the performing life, the wide range of nineteenth-century audiences and their gradual transformation from boisterous participators to respectful listeners, and the establishment of the piano recital as it exists today. With the colorful personalities of the pianists, the juxtaposition of high art and unsophisticated audiences, and the predilection of Americans to treat even the most serious subjects with humor, the book is illuminating and entertaining. The text is illustrated with ads, newspaper clippings, and correspondence that bring to life this collision of cultures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".