Bibliographic record
Abstract
The crossroads where the music of Broadway met popular culture was an expansive and pervasive juncture throughout most of the twentieth century and continues to influence the cultural discourse of today. Broadway to Main Street: How Show Music Enchanted America details how Americans heard the music from Broadway on every Main Street across the country over the last 125 years, from sheet music, radio, and recordings to television and the Internet. The original Broadway cast album—from the 78 rpm recording of Oklahoma! to the digital download of Hamilton—is one of the most successful, yet undervalued, genres in the history of popular recording. The phenomenon of how show tunes penetrated the American consciousness came not only from the original cast albums but from interpreters such as Frank Sinatra and Barbra Streisand, impresarios such as Rudy Vallee and Ed Sullivan, and record producers such as Johnny Mercer and Goddard Lieberson. The history of Broadway music is also the history of American popular music; the technological, commercial, and marketing forces of communications and media over the last century were inextricably bound up in the enterprise of bringing the musical gems of New York’s Theater District to millions of listeners from Trenton to Tacoma, and from Tallahassee to Toronto.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.781 | 0.469 |
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".