“Does Anybody Have A Map?”: The Impact of “Virtual Broadway” on Musical Theater Composition
Bibliographic record
Abstract
Pasek and Justin Paul identify a growing online culture within musical theater, terming this space "Virtual Broadway."This term refers to the virtual arena within which musical theater composers and audiences directly interact, necessitating explorations of how this democratization of the mediation process has impacted musical theater composition and consumption.In popular culture, "There are no longer subjective gatekeepers controlling who gets let 'in,' promoted and exposed.The choice is ours.Now, anyone can be famous" (Price).This transformation is evident in musical theater, where an upsurge in "YouTube musical theater composers" (Pasek and Paul) and social media engagement challenges the dominance of the book musical.Opportunities for self-promotion on the Internet are vast and allow composers to reach a more diverse audience.These emerging opportunities subsequently influence the form of produced works.With humans online having an average attention span of eight seconds (Microsoft Canada 6), this article considers how musical theater is evolving to meet the requirements of millennials and Gen Z.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".