Understanding Muslims’ interactions with non-Muslims: Laying the foundation for culturally sensitive social work engagement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.021 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it