The Social and Economic Integration of Highly Skilled Muslim Immigrants in the Canadian Knowledge Economy
Bibliographic record
Abstract
This study explores the social and economic integration outcomes and lived experiences of highly skilled Muslim immigrants in Canada. It investigates the effect of race and religion on their experiences of socio-economic integration and examines their strategies of resistance in response to the challenges. An overall aim was to critically understand the human capital-based integration discourse as it relates to their lived realities. Research has shown that Muslim immigrants in Canada experience low economic outcomes compared to non-Muslim Canadians and are the target of a rise in hate crimes due to anti-Muslim sentiment. There is a substantive body of mainly quantitative literature on the integration of highly skilled immigrants in Canada, as it pertains to their successful settlement processes. However, there is limited data and research on the socio-economic integration of highly skilled Muslim immigrants. This study conducted twenty-one qualitative, semi-structured interviews and applied the theoretical perspectives of Anti-colonial Discursive Framework in combination with Muhammad Iqbal’s concept of Khudi (Self). The study found that Muslim immigrants are at a clear and significant disadvantage in the Canadian labour market facing Islamophobia and racism in hiring, retention, and daily social interactions. All participants experienced the non- recognition of their foreign credentials and skills, a demand for Canadian experience, occupational downgrading, and received no resume call backs. Muslim immigrants show a strong sense of Khudi (Self) and identity in the face of these challenges. They invoke resistance and agency through a reliance on indigenous knowledge and faith-based strategies. They also showed a high sense of belonging to Canada and engagement in the community through political participation and volunteering.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".