Bibliographic record
Abstract
Because the arts industry is crucial to our self-image, we are naturally curious not only about its size, but also about its rate of growth. If it is growing rapidly, we are likely to think better of the state of our society than if it is growing slowly or not at all. We begin this chapter by tracing the growth of the live performing arts in the United States since 1929, with special attention to the impact of technological innovation. We then examine recent trends in Canada, Australia, and Western Europe. In this context, the principal forms of the live performing arts – theater, symphony, opera, and dance – can readily be analyzed in common. Growth of activity in the fine arts and the growth of art museums is taken up separately in Chapters 9 and 10. Although we may all agree that the arts are more than “mere entertainment,” they are a form of entertainment, nevertheless, and must compete with its other forms in the budgets of interested consumers. The historical perspective adopted in this chapter allows us not only to measure the arts' long-run growth, but also to see how they have fared in competition with other kinds of recreation, and especially with other forms of spectator entertainment. In addition, it shows us how well the live arts have stood up against the endless flow of technological innovations, from talking pictures through television to the compact disc and the videocassette recorder, that have transformed the nonlive entertainment industry over the same span of years.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".