Resisting Islamophobia: Muslims Seeking American Integration Through Spiritual Growth, Community Organizing and Political Activism
Bibliographic record
Abstract
Since 9/11, second-generation Muslims have experienced an increase in religious discrimination that has presented several challenges to their American integration. Scholars have noted that Muslims are often marginalized and “othered” because of their religious beliefs, attire choices and non-Western ethnic origins. In New York, Arabs, South Asians and Africans are the predominant ethnic groups practicing Islam. Although Muslim communities are ethnically and racially diverse, they are categorized in ways that have transformed their religious identity into a racialized group. This new form of racial amalgamation is not constructed on underlying skin color similarities but on their religious adherence to Islam. The War on Terror has complicated the image of Muslims by circulating Islamophobia, or the fear of Muslims and Islam, onto American society. Political rhetoric targeting Muslim communities has also incited new ways of misinterpreting Qur’anic text to further marginalize them. Second-generation Muslim Americans are responding to Islamophobia by reframing the negative depictions about their identities through community-based activism. This paper takes an intersectionality approach to understanding how Muslims across the New York metro area are managing their religious identities as they seek to develop a sense of belonging in American society. This ethnographic case study addresses how second-generation Muslims are resisting Islamophobia through community building, civic engagement, and college student associations. Countering Islamophobia has become part of the everyday life experience for Muslims in New York and is currently their main trajectory for integration into American society.
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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 | 0.000 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".