Hybrid styles, interstitial spaces, and the digital advocacy of the Salafi feminist
Bibliographic record
Abstract
This article examines the online advocacy work of Zainab bint Younus, a Canadian Muslim blogger who identifies herself online as the Salafi Feminist. In 2015, bint Younus curated a series of self-portraits from women who wear the niqab, the Islamic face veil. These photos show the women engaging with Western consumerism and popular culture, but they also employ the blended visual styles and the hybridity of digital spaces to deconstruct dominant binaries of Muslim women. While niqabis discuss being treated as sub-human in public spaces because of their covered faces, the digital media provide a creative space to speak back and demonstrate their agency. However, these digital projects go beyond simply creating a space of expression, as these Muslim women engage with tactics of hybridity, mimicry, and disidentification to work within Western cultural spaces, such as selfies, social media posts, and consumer sites, to destabilize Western feminist notions of the liberal, agentive subject. These photos subvert the assumption that self-portraits must show the face, as the women cover their faces with the niqab but illustrate their personalities through other means.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".