Introduction: Transnational Feminism in a Time of Digital Islamophobia
Bibliographic record
Abstract
In the nearly two decades following the events of 9/11, Western mainstream media have become obsessed with Islam, often sensationalizing Muslims as inherently violent, barbaric, and as undesirable Others.In the current technological era, the number of users who have taken to digital media and social networking sites (SNS) to express their anger, hatred, and even to make death threats towards Muslims has been increasing dramatically.Since before and after taking office, Donald Trump has done much to further exacerbate and justify the flames of these hateful pursuits, exemplifying the heightened state of anti-Muslim sentiment in the current digital landscape, in North America and beyond.In this interdisciplinary special issue of Islamophobia Studies Journal, we aim to a) document and make visible in the face of "fake news" and misinformation the various instances of ongoing and virulent Islamophobia and their different transnational itineraries and impacts, but also, and perhaps even more importantly, b) to document how such instances of hate and ignorance can be combatted through various modes of resistance.Specifically, this collection gathers scholarly conversations that come together at the intersection of transnational feminisms and digital Islamophobia in order to better understand how the confluences of race, gender, and religion become transmuted through digital platforms and racist, nationalist, and other violent and lethal Islamophobic ideologies.The five articles assembled here can be loosely grouped into two sections.The first section, including articles by Farokhi, Mirrlees and Ibaid, and Jiwani, document and establish the increasingly dangerous stakes of digital Islamophobia, making important connections between online representations and material effects.In the second section, articles by Hirji and Oumlil offer important antidotes to these trends, drawing attention to important modes of resistance to Islamophobia undertaken by Muslim women across a variety of digital platforms, as well as different political and aesthetic mediums, toward a truly transnational and feminist coalitional politics set against the harms of Islamophobia.Zeinab Farokhi's article, "Cyber Homo Sacer: A Critical Analysis of Cyber Islamophobia in the Wake of the Muslim Ban," provides a critical starting point for these threads of inquiry by tracing the virtual but no less violent rhetoric surrounding the #MuslimBan twitter trend following Trump's rise and attainment of the US presidency.Employing Giorgio Agamben's formulation of "bare life," Farokhi applies Agamben's insights in order to take stock of the ways in which Muslims have been recast as living, on and off-line, in a "state of exception" which increases their vulnerability to verbal, emotional, psychic, and ultimately physical violence.Importantly, Farokhi's article sets a powerful tone regarding the deeply embodied stakes of anti-Muslim sentiment supported by the US president, unfettered by the law, and pursued through the empowerment of "netizen-sovereigns."
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".