Muslim Stand-Up Comedy in the US and the UK: Incongruity, Everydayness, and Performativity
Bibliographic record
Abstract
The objectives of this article are two-fold: to provide a review of the major figures and trends in Muslim American comedy and discuss certain techniques and approaches that have been used by stand-up comedians to counter predominant and discriminatory perceptions of the “Other”. To reiterate film critic Jack Shaheen’s argument in Reel Bad Arabs: How Hollywood Vilifies a People, the Western public is fed constructions of Islam as a “problem”; the terms “Arab” and “Muslim” are interchangeably used; Muslim men are depicted as “terrorists”; and Muslim women are depicted as “veiled and oppressed”. Much has been written on the generation and effect of stereotypes promoted by popular culture. However, stereotyped groups also use popular culture to speak for themselves. Popular culture also functions to resist, counter, push back against, and subvert stereotypes. In other words, the “Other” can speak for him or herself through popular culture as a means of contesting stereotypes that define Muslims and Arabs in terms of cultural and religious understandings that narrowly categorize individuals through attributes such as religiosity and femininity. This potential is being realized by second-generation Muslims familiar with the platforms created and provided by other marginalized groups in Anglo-American popular culture, and their work has come into its own especially in the aftermath of 9/11, a time that saw both the intensification of stereotypes and heightening of Muslim American consciousness. I concentrate on these specific stand-up comedians in the US and the UK, despite the fact that there are others in the diaspora who discuss Islamophobia, because these American and British comedians address all of the three most common stereotypes of Muslims: “Arab = Muslim”, the “terrorist”, and the “veiled and oppressed woman”.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".