Faith, culture, and networks: decoding the resilience of highly skilled African immigrants in Quebec
Bibliographic record
Abstract
Purpose In the context of increasing global migration, understanding the resilience mechanisms of immigrants is crucial for informing both scholarly inquiry and policy development aimed at enhancing community support and integration. This study aims to examine the resilience strategies of highly skilled African immigrants (HSAIs) in Quebec, focusing on how they leverage their religious beliefs, cultural values and social networks to navigate the complexities of the local labour market. Design/methodology/approach Using a qualitative approach, the study reveals a dynamic interplay between spiritual faith and personal agency, illustrating how this combination strengthens HSAIs’ ability to cope with challenges in the Quebec labour market. Findings The findings offer a nuanced perspective on resilience that goes beyond traditional views, emphasizing the role of cultural values such as diligence and perseverance in shaping professional identities and work ethics. Mentorship and volunteerism are identified as key factors in facilitating career advancement and socio-economic integration. Furthermore, the study uncovers psychological drivers – including hope, determination and a survival instinct – that underpin the resilience of HSAIs. By underscoring the multifaceted nature of resilience, influenced by an array of social, cultural and psychological factors, this study provides important insights for policymakers, community leaders and scholars. It advocates for a comprehensive approach to understanding and supporting the resilience of immigrant populations, thereby informing more effective community support initiatives and integration strategies. Originality/value The study provides an original contribution by exploring the resilience of HSAIs in Quebec through the unique lenses of faith, cultural values, mentorship and volunteerism. It expands existing literature by applying an ecological framework to understand resilience in a distinct sociocultural and linguistic context, emphasizing the interplay between personal agency and community support. The findings offer practical insights for policymakers, community leaders and scholars, advocating for tailored integration strategies that leverage the cultural assets of HSAIs. This nuanced approach advances our understanding of immigrant adaptation and socio-economic integration, specifically within Francophone regions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".