Bibliographic record
Abstract
This paper addresses the ways in which Muslim women seek to employ online media, particularly social media, to reclaim narratives around space, embodiment, and power. I argue that digital space is, like any other form of media, structured essentially by racism and patriarchy, but I also note the crucial potential for resistance exhibited by Muslim activists such as political leaders Ilhan Omar and Rashida Tlaib, Instagram influencer Ayesha Malik, and the largely anonymous women who participated in #MosqueMeToo, encouraged by the journalist and activist Mona Eltahawy. I draw upon a post/anti-colonial feminist framework and the tools of critical discourse analysis in examining specific instances where such women perform acts of resistance that, in turn, trigger a gendered and raced reaction. I note the ways in which some Muslim women, such as Saudi teenager Rahaf Mohammed, are constructed as media heroes, given that their stories can be co-opted to validate notions of the white colonial savior, while others directly challenge narratives of colonialism and oppression and are thus subjected to backlash. I point to the ways in which some of this vitriol continues to refer back to the notion that Muslim women should be silent, and to the fetishized Muslim woman's body: how it should look, where it can/should go, and what can be done to it.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".