The digital closet: technology, queer Muslim youth, and the politics of online survival
Bibliographic record
Abstract
This article qualitatively examines how LGBTQIA+ Muslim youth in Canada negotiate identity, belonging, and survival through digital technologies amid family rejection, cultural surveillance, and religious expectations. Drawing on in-depth interviews with six racialized Muslim young adults in the Greater Toronto Area (GTA), the study introduces the concept of the digital closet to describe how digital spaces act as both refuge and risk. Participants depict social media, dating apps, and encrypted conversations as sites for secrecy, intimacy, and self-expression. Rather than following conventional ‘coming out’ narratives centered on visibility, youth relied on flexible disclosure shaped by dread, shame, love, and resilience. The analysis develops the concept of precarious digital intimacies, fragile, yet vital connections sustained online that provide reassurance but remain vulnerable to surveillance and exposure. This article contributes to intersectional queer theory while exposing the limitations of Canadian multiculturalism, which continues to marginalize queer Muslim youth in both ethno-religious and mainstream LGBTQIA+ spaces. Islamophobia further shaped participants’ digital navigation, pushing them away from mainstream queer platforms and toward encrypted, peer-driven networks. The study’s very small sample reflects the sensitivity of the topic and the difficulty for Muslim youth to openly discuss sexuality and queerness, underscoring the importance of trust and ethical care in recruitment. Findings underscore the need for culturally grounded mental health supports, trauma-informed care, and digital literacy programs. Ultimately, survival, secrecy, and conditional intimacy emerge not as deficits but as generative strategies of resilience in digitally mediated diasporic life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".