Islamophobia and Proximities to Whiteness: Organizing Outside of the Brown Muslim Subject
Bibliographic record
Abstract
The existing Islamophobia 1 literature has come to illustrate how the Muslim subject “can at a moment’s notice be erected as [an] object of supervision and discipline” (Morey and Yaqin 2011: 5–6). In the popular imagination, Muslimhood 2 has come to stand for an undifferentiated culturally alien oriental subject defined through the prism of racialized violence and irrationality. Although much of the anti-Islamophobia efforts – academic and community-based – work to combat the reductiveness of a universalized Muslim figure , these efforts tend to uncritically take up the brown Muslim figure as the starting point of inquiry, thereby further reifying the homogenizing racialization of dominant discourses. This article opens up the possibilities to expand thinking on the lifeworld of Islamophobia by addressing the erasure that happens with this homogenizing approach to Islamophobia. In particular, we consider the dialogical nature between the operational life of Islamophobia and the differing proximities to whiteness our intersectional subject positions make available. And in turn, how these availabilities come to shape the experience of Islamophobia is a prime focus of analysis. The authors ask: how does the systemic demarcation of Muslim subjectivity, across racial, ethnic, class, regional, and ideological lines, interact with how Islamophobia is experienced and operationalized? Leveraging an auto-ethnographic approach, we provide first-person narratives of Islamophobic encounters from our respective geopolitical and social locations to deconstruct and delineate an intersectional understanding of Islamophobia.
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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 | 0.001 | 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 | 0.000 | 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".