Voiceless Icons No More: the Self-Representations of Muslim American Lives in Digital Media
Bibliographic record
Abstract
With the proposed travel bans in the U.S., niqab bans in Canadian cities, fears over terrorism, attacks on veiled women, and concerns about refugees, Muslim Americans have become prominent icons of larger political issues in North American society. While the images of Muslims have dominated public spaces, the complex lives and significant concerns of Muslim Americans have often gone untold. This study focuses on the creative work that Muslim American youth are producing and circulating through digital media spaces and mobile technologies.\nThe projects discussed in this study include: the Places You’ll Pray photo series, which documents public prayer locations; the visual representation of the Salafi Feminist blogger; a collection of self-portraits from women who wear the niqab face veil; the artistic projects created in response to the murder of three Muslim college students in Chapel Hill, NC; and the Mipsterz Islamic fashion video.\nThrough an analysis of these cases, this dissertation examines how Muslim American youth create innovative projects that engage with aesthetic styles and affects to shift assumptions about their lives and dis-articulate the feelings of foreignness and fear that often adhere to their bodies. These projects are an effort for Muslims, who have been marginalized from traditional political spaces, to assert the equality and value of their lives. Without over-idealizing the progressive potential of emerging media spaces, this study examines how digital media provide the flexible space and creative tools to produce and circulate these representations.\nUsing digital media to create and distribute artistic projects is not political activism in and of itself, but rather it serves as a way for Muslim youth to formulate and articulate their own subjectivity as political actors and to connect with others in preparation for wider social action. Through this creative work, Muslim youth acquire a sense of their agency and ability to intercede in political spaces. Furthermore, these Muslim creators are not trying to assimilate into an American way of life, but rather assert that they can determine for themselves how they will be Muslim and American, along with numerous other markers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".