The Virtual Killing of Muslims: Digital War Games, Islamophobia, and the Global War on Terror
Bibliographic record
Abstract
This article argues that digital war games communicate misleading stereotypes about Muslims that prop up patriarchal militarism and Islamophobia in the context of the US-led Global War on Terror. The article's first section establishes the relevance of the study of digital war games to feminist games studies, feminist international relations, and post-colonial feminism. The second section contextualizes the contemporary production and consumption of digital war games with regard to the “military-digital-games complex” and real and simulated military violence against Muslims, focusing especially on the US military deployment of digital war games to train soldiers to kill in real wars across Muslim majority countries. The third section probes “mythical Muslim” stereotypes in ten popular digital war games released between 2001 and 2012:Conflict: Desert Storm(2002),Conflict: Desert Storm 2(2003),SOCOM U.S. Navy SEALs(2002),Full Spectrum Warrior(2004),Close Combat: First to Fight(2005),Battlefield 3(2011),Army of Two(2008),Call of Duty 4: Modern Warfare(2007),Medal of Honor(2010), andMedal of Honor: Warfighter(2012). These games immerse players in patriarchal fantasies of “militarized masculinity” and place a “mythical Muslim” before their weaponized gaze to be virtually killed in the name of US and global security. The conclusion discusses the stakes of the stereotyping and othering of Muslims by digital war games, and highlights some challenges to Islamophobia in the digital games industry.
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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 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".