Understanding Muslims’ interactions with non-Muslims: Laying the foundation for culturally sensitive social work engagement
Bibliographic record
Abstract
The North American Muslim population is growing rapidly, but little research has been conducted to help social workers interact with members of this population in a culturally sensitive manner. To assist social workers engage with Muslims in an ethical and effective manner, this qualitative study sought to answer the following questions: how do Muslims experience interactions with non-Muslims and what have they learned from their encounters that might facilitate positive interactions? To answer these two questions, we used narrative inquiry with a sample of 10 Muslim social work students and recent alumni. The findings suggest that Muslims may be treated either positively or negatively by non-Muslims in interactions in various contexts, that they are frequently unable to voice their religious perspectives, and that their religious difference is often portrayed in single-sided or negative ways as well as prioritized against their wishes while ignoring other aspects of their social identities. As a result, many tend to avoid interactions with non-Muslims. The paper offers strategies to foster more respectful interactions with Muslims, such as attending to how much their religious difference is prioritized, and providing opportunities to share their perspectives.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".