Exploring a Muslim Congregation and Leadership in Refugee Resettlement: A Case Study of Mosque Refugee Sponsorship in Montreal
Bibliographic record
Abstract
This doctoral thesis examines the engagement of Muslim congregations in refugee resettlement, with a focus on the Dorval Mosque in Montreal as a case study. Situated at the intersection of congregational studies, refugee studies, and social work, the research employs a qualitative methodology, including key informant interviews, observation and documentary analysis, to investigate the mosque’s decade-long refugee sponsorship initiatives. Drawing on theoretical frameworks such as leadership-as-practice, transformative leadership, and congregational studies, the study explores the mosque’s role as a community hub in navigating the complexities of refugee sponsorship.The findings reveal the pivotal role of mosque leadership in fostering social cohesion, addressing the multifaceted needs of refugees—such as housing, education, and employment—and bridging cultural gaps in the integration process. Leadership at the Dorval Mosque exemplifies flexibility, participation, and a quest for meaning within the framework of Islamic congregational values. However, the study also highlights critical challenges, including financial constraints, reliance on a small donor base, and declining engagement from younger generations, which pose significant risks to the mosque’s future sustainability.The analysis contributes to theoretical discussions by positioning religious congregations as critical actors in Canadian immigration policy and refugee resettlement practices. It also underscores the implications for social work theory and practice, advocating for greater collaboration between faith-based organizations and governmental bodies to enhance refugee resettlement efforts. This research ultimately demonstrates the transformative potential of religious congregations in promoting civic engagement, social justice, and long-term refugee integration while raising awareness about the ongoing challenges faced by these institutions in a rapidly changing societal landscape
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".