Believing without Belonging? The Effects of Racial Discrimination at the Mosque on Religiosity and Mosque Attendance through Belonging for Black Muslims
Bibliographic record
Abstract
Churches have often offered a place of belonging for Black Christians and protected many from the negative impacts of external racial discrimination, which can make Black Christians more likely to engage in communal and private religiosity. Mosques in America are generally more ethnically diverse than churches. Therefore, mosques might not serve as sanctuaries for Black Muslims the way churches may for Black Christians because anti-Black discrimination often manifests within mosques from fellow Muslims. It appears, to date, that only one quantitative study has described interracial relations among American Muslims. We examined whether belonging explains the impact of racial discrimination in mosques on mosque attendance and religiosity for Black Muslims. Black American Muslims responded to anonymous online questionnaires regarding discrimination and belonging felt at mosques as well as religiosity and mosque attendance. Two parallel mediations showed that although belonging fully explained the negative relation between perceived discrimination at the mosque and mosque attendance, it only partially explained the negative relation between perceived discrimination and religiosity. Taking belonging into account, discrimination had a positive effect on religiosity, perhaps via religious coping. Findings are discussed in light of the believing without belonging theory , whereby communal religious engagement decreases, while individual religious engagement increases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".