<i>Onnagata</i>, Grotesque Beauty, and Aging: Reading Tennessee Williams’s Kabuki-Inspired Plays
Bibliographic record
Abstract
This article examines Tennessee Williams’s kabuki-inspired plays, which were written after his first trip to Japan in 1959. I focus on And Tell Sad Stories of the Deaths of Queens… (1957–70), which Williams began writing in 1957 but completed after his trip to Japan, and the 1964 version of The Milk Train Doesn’t Stop Here Anymore, a play that Williams rewrote several times from 1962 to 1964. In so doing, I demonstrate how Williams used and modified kabuki traditions under the guidance of his Japanese friend, the acclaimed novelist and playwright Yukio Mishima. In And Tell Sad Stories of the Deaths of Queens … the art of acting by the onnagata (male actors who play female roles in kabuki), especially those who live as “women” even off stage, underlies the male protagonist’s exploration of transgender identity as well as his female gender presentation. In The Milk Train Doesn’t Stop Here Anymore, Williams seeks to transform the perceived grotesqueries of aging into allure by using kabuki’s aesthetic principles of “grotesque beauty” and “necrophilic” nostalgia, which had also been expressed in Hollywood films featuring older movie actresses. He deliberately wrote the role of the female protagonist for Tallulah Bankhead, who starred in the 1964 production, with the intention of celebrating her aging body through kabuki aesthetics.
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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 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".