Hijabi vloggers: Muslim women’s self expression and identity articulation on YouTube
Bibliographic record
Abstract
The hijab is often cited as a manifestation of Islam’s patriarchy. The advent of mobile technology and social media platforms gave an antidote to this problem. Particularly, vlogging trend among Muslim women lets them disrupt problematic narratives about themselves, speak in their own voices to a global audience and demonstrate their agency. However, the women’s focus on fashion puts them at the mercy of cultural and profit-driven norms. Their use of YouTube also means the vloggers are unconsciously conforming to prevailing trends. This research applies a feminist CDA to illuminate ideologies that shape the women’s articulation of their identities in relation to their ethno-religious communities. The small stories approach of interviews with Muslim women vloggers unearthed this trend’s liberative qualities and pitfalls. Since digital self-representation among marginalized identities like Muslim women is new, this research calls for further research into the utility of digital platforms as tools for identity articulation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".