Bibliographic record
Abstract
This book was inspired by a profusion of sources. One was provided by Ji Hyon (Kayla) Yuh, a Korean student in my 2008 musical theatre seminar who wrote a final paper about a Seoul production of Michael Bennett’s Dreamgirls (1981), which was in fact a far-flung, out-of-town tryout for an upcoming New York revival. I was intrigued, to say the least, to learn how Korean performers had transformed a musical I always assumed to be too embedded in US racial politics to travel. A second was Barrie Kosky’s super-gay 2008 production of Cole Porter’s Kiss Me, Kate (1948) at the Komische Oper Berlin, with which I was completely intoxicated. The production’s scale—conceptually, musically, theatrically—was so mind-blowing that I wanted all my musical theatre friends to drop what they were doing and fly to Berlin to see it. A third spark for the book came from Ken Nielsen, one of my first students at the CUNY Graduate Center, who in 2011 completed a dissertation on productions of Tony Kushner’s Angels in America (1993) in Denmark and Germany. Ken’s project helped me to recognize that studying the reception of US-American theatre abroad could provide fascinating insights into the love/hate relationship between US culture and the rest of the world.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.450 | 0.265 |
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".