“My Skin Is Not Me”: The Transformations of William Shakespeare’s Othello in Ann-Marie MacDonald’s <i>Goodnight Desdemona (Good Morning Juliet)</i> and Djanet Sears’s <i>Harlem Duet</i>
Bibliographic record
Abstract
Abstract Both Goodnight Desdemona (Good Morning Juliet) (1990) and Harlem Duet (1997) are Canadian feminist appropriations of William Shakespeare. Both deal, at least partly, with Othello, and both can be considered subversive re-visions of Shakespeare’s play which aim to articulate oppositional intervention in the canon. These similarities notwithstanding, the plays have not often been studied concurrently. Also, while several critics have explored them, mostly separately, in terms of their adaptation/appropriation of Shakespeare, seeking to spell out the transformations they have brought to the “original” text, little has been said about how the iconic figure of Shakespeare still holds sway in these new dramas, albeit in different ways and to varying degrees. Likewise, their dramatization of the character of Othello remains rather understudied. This essay explores the “new” Othellos of the two plays, contending that their positioning in the two texts evinces some similarities while their characterization differs widely, given the plays’ generic difference, but mostly the two playwrights’ rather divergent feminist perspectives which, in turn, substantially shape the plays’ respective appropriation techniques.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".