Bibliographic record
Abstract
Ever After is more than a detailed show-by-show history of the last quarter century in American musical theater. It explains how the storied Broadway tradition, in many cases, went so very wrong. Singer takes the reader behind the scenes for an unparalleled look at A Chorus Line’s final bow, the creation of Rent, the real people behind Disney's uber-musicals, and even an afternoon with Andrew Lloyd Webber. Ever After also celebrates the promise of the next generation of young musical theater artists, especially Adam Guettel, Michael John, LaChiusa Ricky, Ian Gordon, and Jason Robert Brown, addressing not only their work to date but their future projects. There is no other book currently available that covers this period and subject. Through his work for The New York Times, Singer has interviewed virtually everyone of significance. They are all here, very much speaking for themselves. Ever After is both anecdotal and analytical, featuring personality profiles of important creative figures from Jule Styne to Stephen Sondheim to Jonathan Larson, while critically evaluating all of the many musicals produced during the past 25 years. Sure to generate debate, this is a book written not only for the musical theater aficionado, but for anyone who has seen a Broadway musical or has just enjoyed the movie version of Chicago and is curious to know more.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.781 | 0.698 |
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".