Hollywood’s Bad Muslims: Misrepresentations and the Channeling of Racial Violence
Bibliographic record
Abstract
The cinemas of Arab and Muslim societies encompass a substantial number of film genres produced locally or in the diaspora. Arab and Muslim filmmakers experiment with different cinematic narratives, styles, and hybrid forms: auteur, documentary, diasporic, migrant, Third Cinema, and transnational productions. Their richness, diverse thematic foci, creative stylistic characteristics, and ability to reach global audiences recently motivated film scholars and other academics in Europe and the United States to consider designating a category called “Muslim Cinema” and defining its contours. The influence of these rich cinemas in contesting Hollywood’s demonization of Muslims, the conflation of Arabs, Muslims, and Islam, and the proliferation of anti-Muslim racism in Western discourse, however, remains very limited. Therefore, this article argues that the idea of such a category, if one were to be created, should explore venues to address Hollywood’s evolving forms of racializing Muslims and their relationship with the current institutionalization of anti-Muslim racism in the United States. Through a brief survey of Hollywood’s contemporary productions about Muslims, this article analyzes the impact of moving images on representation, particularly the fossilized characterization of Muslims as evil, and identifies three areas in American cinema and political discourse that could belong to this category: the first is Hollywood’s uninterrupted flow of making essentializing and essentialized narratives that conflate Arabs, Muslims, and Islam, and normalizes violence against them; the second deals with the transition from Islamophobia to anti-Muslim racism and explains its sanctioning by the US government; the third addresses the morphing of Islam into a race.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".