The sense of an ending in The Hunchback of Notre Dame: A conversation with Sarah Langford and Nicholas Cunha
Bibliographic record
Abstract
In this article, I discuss with Sarah Langford and Nicholas Cunha their new production of The Hunchback of Notre Dame, an adaptation that brings together Victor Hugo’s 1831 novel and music from Gary Trousdale’s and Kirk Wise’s Disney classic (1996). A succession of adaptations, including hits like Kenneth Branagh’s Cinderella (2015) and Bill Condon’s Beauty and the Beast (2017) and the musicals Aladdin (2011), Newsies (2011) and Frozen (2017), has brought new life to Disney features. In this climate, the Toronto-based Wavestage Theatre Co.’s production of Hunchback offers a significant departure through its incorporation of the darker elements from Hugo’s original. This musical’s concerns regarding (in)tolerance, desire and diversity make it especially resonant. In what follows, Langford, Cunha and I discuss the process of producing this musical, how it reads Quasimodo and his love for Esmeralda and the Archdeacon Dom Claude Frollo and how Hunchback speaks to today’s audiences.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.034 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".