From Social Justice to Metaphor: The Whitening of Othello in the Russian Imagination
Bibliographic record
Abstract
Othello was the most often-staged Shakespeare play on early Soviet stages, to a large extent because of its ideological utility. Interpreted with close attention to racial conflict, this play came to symbolize, for Soviet theatres and audiences, the destructive racism of the West in contrast with Soviet egalitarianism. In the first decades of the twenty-first century, however, it is not unusual for Russian theatres to stage Othello as a white character, thus eliminating the theme of race from the productions. To make sense of the change in the Russian tradition of staging Othello, this article traces the interpretations and metatheatrical uses of this character from the early Soviet period to the present day. I argue that the Soviet tradition of staging Othello in blackface effectively prevented the use of the play for exploring the racial tensions within the Soviet Union itself, and gradually transformed the protagonist’s blackness into a generalized metaphor of oppression. As post-collapse Russia embraced whiteness as a category, Othello’s blackness became a prop that was entirely decoupled from race and made available for appropriation by ethnically Slavic actors and characters. The case of Russia demonstrates that staging Othello in blackface, even when the initial stated goals are those of racial equality, can serve a cultural fantasy of blackness as a versatile and disposable mask placed over a white face.
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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 | 0.002 | 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 | 0.009 | 0.022 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".