Performing Anti-Muslim Racism; Or, Muslim Self-Fashioning in Ayad Akhtar’s Plays
Bibliographic record
Abstract
Ayad Akhtar claimed in an interview after a performance of his Pulitzer-winning play Disgraced that the play was about the ways Muslims “are still beholden on an ontological level to the ways in which the West is seeing us.” The point is for Muslims to free themselves from those networks of symbolic identification by reclaiming their voices and telling their own stories. The claim, then, is that the performance of Muslimness that Akhtar is initiating on the American stage with his critically acclaimed plays allows Muslims to escape the rules and demands of the hegemonic play of identities. How, then, does one redefine Muslimness while performing it? Situated in this context, this paper examines the narrative possibilities and possible narratives that are available to Muslim characters on the American stage. This article will read two of Akhtar’s plays, Disgraced (2013) and The Who & The What (2014), through the lens of this purported claim of ontological reconstruction. Following Akhtar’s admission that the latter was inspired by William Shakespeare’s The Taming of the Shrew, I will extend the same principle of adaptability to read the former as a contemporary retelling of Othello. As a critique pivoted on the triple axes of class, gender, and form, this article will argue that by deploying the same ideological positions and stereotypical narratemes that legitimize anti-Muslim racism in America in the larger project of the US Empire, Akhtar leaves his self-endowed task of ontological reconstruction both politically and ethically wanting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".