Bibliographic record
Abstract
The early drama of Eugene O'Neill, with its emphasis on racial themes and conflicts, opened up extraordinary opportunities for Black performers to challenge racist structures in modern theater and cinema. By adapting O'Neill's dramatic writing—changing scripts to omit offensive epithets, inserting African American music and dance, or including citations of Black internationalism--theater artists of color have used O'Neill's texts to raze barriers in American and transatlantic theater. Challenging the widely accepted idea that Broadway was the white-hot creative engine of U.S. theater during the early 20th century, author Katie N. Johnson reveals a far more complex system of exchanges between the Broadway establishment and a vibrant Black theater scene in New York and beyond to chart a new history of American and transnational theater. In spite of their dichotomous (and at times problematic) representation of Blackness, O'Neill's plays such as The Emperor Jones and All God's Chillun Got Wings make ideal case studies because of the way these works stimulated traffic between Broadway and Harlem—and between white and Black America. These investigations of O'Neill and Broadway productions are enriched by the vibrant transnational exchange found in early to mid-20th century artistic production. Anchored in archival research, Racing the Great White Way recovers not only vital lost performance histories, but also the layered contexts for performing bodies across the Black Atlantic and the Circum-Atlantic.
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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 | 0.000 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".