Cyber Homo Sacer: A Critical Analysis of Cyber Islamophobia in the Wake of the Muslim Ban
Bibliographic record
Abstract
This article takes up Giorgio Agamben's formulation of “bare life” (1998) and applies it to the contemporary perpetuation of violent Islamophobia in online spaces, producing what I term a figuration of the (Muslim) cyber homo sacer . Particularly focusing upon the proliferation of virulent, anti-Muslim rhetoric and discourse on Twitter, I follow the hashtags #Muslimban and #BanMuslim to demonstrate how Agamben's concepts of homo sacer , state of exception, and the camp—though with important differences—helpfully illuminate the ways in which current Islamophobic and anti-Muslim sentiment online can be understand as a refiguration of Muslims as bodies which exist in a state of in-betweenness. In this “state of exception,” Muslims become more vulnerable to verbal, emotional, psychic, and ultimately physical violence, at the same time as they become less recognizable to the policies and laws which should, ostensibly, protect them.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.043 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| 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".