A Multiethnic <i>Streetcar Named Desire:</i> We’ve Had This Date from the Beginning
Bibliographic record
Abstract
ABSTRACT: Stephen Byrd’s 2012 Broadway production of Tennessee Williams’s A Streetcar Named Desire, with black and Hispanic actors, inspired the typical strong responses from critics, many of whom objected to the casting as historically improbable. Yet it is precisely the predictability of these responses that argues in favour of non-traditional casting. Such casting reveals something new about the play and also about audience assumptions. It forces a confrontation with ideology that is particularly revealing in the case of Streetcar. When director Emily Mann cast African-American and Hispanic actors in the lead roles, she denied theatregoers the opportunity to ride the tracks up one old narrow street and down another to a familiar destination – an interpretation that both scapegoats and romanticizes the South, exposing and concealing the nation’s racial history. Byrd and Mann proved, not only that Streetcar can be successfully produced with a multiethnic cast, but that it demands to be done so.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".