Bibliographic record
Abstract
Musical theatre can take root anywhere. The future of the musical may very well lie beyond Broadway and the West End. In recent years, successful musicals have been developed in Canada, Australia and the German speaking countries. Some, like Elisabeth, have travelled internationally without ever playing in English. Companies in Korea, Japan and China are investing in new works, both domestically and internationally. These different countries can learn from each other. In South Africa, people do literally burst into song on the streets. During the apartheid era, some of the freedom fighters were known to have gone to the gallows singing. Both there and in Argentina, musical theatre played an active role in the struggle against oppression. Shows like Sarafina weren’t just about the struggle against apartheid, they were part of it. This is nothing new – the cabarets of Weimar Berlin were also struggling against oppression. In fact, the birth of the musical coincided with the birth of democracy. On the other hand, during World War II, the all-female Takarazuka Revue was co-opted by the Japanese government for propaganda purposes. The real point of my book A Million Miles from Broadway is not just to tell a history of the musical. It’s what you do with that history after you’ve learned it that is important. Firstly to learn about our own musical theatre heritage, but also to learn about each other’s. We may find that people in other countries have found solutions to problems that we are struggling with.
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 | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.509 | 0.232 |
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".