Rendering Whiteness Palatable: The Acceptable Muslim in an Era of White Rage
Bibliographic record
Abstract
In this paper, I analyze the perspectives of the Acceptable Muslim (Kassam, 2018)in two Canadian case studies: (a) Irshad Manji, a Canadian Muslim journalist and activist who has been an active commentator on a variety of issues including those related to Muslims; and (b) the CBC sitcom Little Mosque on the Prairie (2007-2012), which was the first Canadian mainstream television series featuring Muslim characters. I suggest that these case studies illuminate the figure of the Acceptable Muslim (Kassam, 2018)who is represented as a “moderate,” modern, and assimilable Muslim, and who espouses a privatized faith with few public expressions of religious/cultural belonging. Centrally implicated in Canadian debates about multiculturalism, gender equality, citizenship, and secularism, Acceptable Muslims (re)confirm the racial boundaries of the nation-state, becoming icons of multiculturalism, reanimating the whiteness at the heart of the Canadian nation-state. The Acceptable Muslim sustains the narrative of the Canadian nation-state as liberal, secular, modern, and inclusive even as it relentlessly excludes, punishes, and eliminates the Muslim Other, enabling such policies to be legitimated as “race-neutral.” Acceptable Muslims stand as sentries at the (symbolic) borders of the nation, reanimating racialized boundaries of acceptability and signalling that those beyond these boundaries can be legitimately policed by the nation-state. My analysis provides insights into how Canada has re-configured the power and persistence of its white fantasy and, through the strategic use of the Acceptable Muslim, cloaks its deeply racialized coding in more palatable grammars of multiculturalism, gender equality, and secularism.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.063 | 0.062 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".