Bibliographic record
Abstract
Abstract This chapter relates the story behind Sousatzka, another Maltby/Shire musical that has been orbiting New York but hasn’t yet landed. Sousatzka, with a libretto by Craig Lucas, is an adaption of the novel Madame Sousatzka by Bernice Rubens (also the source material for the 1988 movie of the same name starring Shirley MacLaine). The musical was the brainchild of producer Garth Drabinsky, his first project after he completed his seventeen-month prison term. Sousatzka’s Toronto production received mixed reviews, but Drabinsky remained determined to bring it to Broadway. After the failure in 2022 of Paradise Square, another Drabinsky-produced Broadway musical, the future prospects for Sousatzka are dim. The Toronto production featured Tony winner Victoria Clark in the title role, orchestrations by Jonathan Tunick (with whom Drabinsky had an infamous falling-out), and additional arrangements by South African composer Lebo M, who also contributed arrangements to The Lion King (movie and musical).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.074 | 0.016 |
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".