The Public Theater and WNYC Studios, producers; Saheem, Ali, dir. Romeo y Julieta
Bibliographic record
Abstract
From the onset of The Public Theater's podcast, Romeo y Julieta, the primacy of the Spanish language is made evident.The Chorus, read by Cuban actor Tony Plana, boldly announces, "Dos familias, both alike in dignity, / En Verona, escenario gentil." Most audiences know the English version of this opening passage from memory, but for me, the Spanish hits so much harder, and I will elaborate on this below.Here, I will say that I found this to be an exceptional podcast; the sound quality of Romeo y Julieta is polished, and the podcast is accessible to audiences through manifold means (through on-demand listening on the Romeo y Julieta website, YouTube, and a host of podcast apps, where it is also available for download).At nearly two and a half hours, the length of the podcast is comparable to that of a staged play.But the emphasis in a podcast, of course, is on listening, and the unique sounds of this podcast hold the audience's attention from start to finish.Bilingual adaptations of Romeo and Juliet are not uncommon, perhaps due in part to the popularity and influence of West Side Story.But while the early cinematic adaptation of that musical makes me cringe at the brownface and the long, enduring tradition of gatekeeping when it comes to Latinxs in Hollywood, The Public Theater's production deliberately centers Latinx actors and voices.Directed by Saheem Ali and adapted in collaboration with Ricardo Pérez González, this bilingual play is based on Alfredo Michel Modenessi's keen Spanish translation.For audience members like me, who grew up in Spanishspeaking households and practised code-switching on a consistent basis, the sounds of this adaptation are refreshingly familiar.Listening to this podcast, Shakespeare feels like home.And it is not merely the sound of the Spanish language that makes it feel comfortable but rather the way that it is employed.Intermixed with English, the Spanish language is playful, serious, angry, sad, and loving, and often all at once.In other words, it is not a facile translation of Shakespeare's English into Spanish, nor is it a play where Spanish is peppered here and there throughout.Instead, it is a production that offers a thoughtful approach to capturing the ethos of Spanish-speaking communities.Bawdy
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.019 |
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".