Why Natasha Can’t Sing: Rethinking Cross-Genre Adaptation with <i>Natasha, Pierre & the Great Comet of 1812</i>
Bibliographic record
Abstract
This article uses Natasha, Pierre & the Great Comet of 1812, Dave Malloy’s 2016 musical based on Leo Tolstoy’s War and Peace, to explore dynamics that arise when novels are adapted for the stage. I first identify the challenge of adaptational interference: an effect that leads adaptations to suppress features of their source texts that are too resonant with the genre of the adaptation. Natasha, Pierre exemplifies adaptational interference through its heroine. In War and Peace, Natasha has a beautiful singing voice. A musical might seem ideally poised to celebrate this feature, but the ubiquity of singing in musical theatre prevents Natasha, Pierre from highlighting it. I then explore how, as a counterweight to adaptational interference, adaptations can make spectators “knowing” about how they engage with their source texts. My discussion responds to Linda Hutcheon’s well-known theory of adaptation: Hutcheon holds that one must be familiar with an adaptation’s source text to consume it “knowingly” – that is, with an eye toward how it relates to its source text. I show how Natasha, Pierre induces knowingness on the spot through content that evokes the genre of its source text. Past criticism has tended to focus on how similarities between genres facilitate adaptations. I suggest that distinctions between genres may be more beneficial to adaptations, enabling them to re-present texts in ways both formally ingenious and clearly marked as a kind of artistry that only adaptations can achieve.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".