Bibliographic record
Abstract
Abstract Islam in North America is an incredibly diverse phenomenon with a long history and a range of different perspectives on what “American/Canadian Islam” is or should be. While the presence of Islam in the United States dates back to the transatlantic slave trade, Muslim identity in the region is often linked to an immigrant presence and became synonymous with a sense of violent foreignness after the 9/11 attacks. Mainstream Western media has played a fundamental role in the configuration of Islam as the ultimate cultural “other,” leaving Muslims who strongly identify as Muslim and American or Canadian in a precarious position. Representation and debates around Muslim identity have recently shifted to online platforms. Social media has not only impacted how Islam is practiced in the United States and Canada but has also influenced self-presentation, community building, and activism among Muslims across ethnicity, race, generation, and class. From Quranic websites and Muslim dating apps to blogs, Instagram influencers, and Snapchat fatwas, North American Islam has developed a burgeoning presence across the digital landscape. Furthermore, social media provides a central space through which national politics and policies play out, and Muslims in particular have faced challenges ranging from Islamophobia and religious persecution to digital surveillance and censorship. Such phenomena have impacted the online activities of Muslims and deeply inform the day-to-day lives of Muslim communities across the region. Through exploring the various ways in which Muslims in these minority contexts experience the growing interrelationship between their on- and offline lives, we approach the digital as a space where culture is continually produced, performed, and contested.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".