The Labour Market Experiences of African Immigrants in Saskatoon Through an Intersectional Lens
Bibliographic record
Abstract
ABSTRACT Despite high levels of education and extensive work experience, African immigrants in Canada often struggle to secure jobs that align with their qualifications, particularly in their early years of settlement. This dissertation sets out to examine the labour market experiences of African immigrants in Saskatoon, focusing on three key areas: their general labour market experiences, how intersecting identities such as race, gender, religion, and family status shape these experiences, and the agency and strategies they employ to improve their employment outcomes. The study used intersectionality as its theoretical and analytical framework to critically examine how multiple overlapping systems of power, such as racism, sexism, and religious discrimination, produce differentiated labour market outcomes. A qualitative research design was adopted. Data were collected through thirty-one semi-structured interviews with African immigrants (16 men, 15 women) who were residents of Saskatoon and had lived in Canada for at least five years. Data were analyzed thematically using NVivo software. The results reveal that the labour market experiences of African immigrants in Saskatoon are deeply racialized, shaped by structural and identity-based exclusions, and intensified by the unique challenges of settling in a mid-sized city. Both African men and women experienced deskilling and downward occupational mobility, although gender, religion, and family status significantly shaped these challenges. Women with younger children faced greater constraints, and Muslim women experienced the most pronounced disadvantage due to the combined effects of race, religion, gender, and immigrant status. The study highlights a paradox: participants are hypervisible in their “otherness” yet invisible when it comes to employment opportunities, generating constant pressure to demonstrate competence and work harder than their peers. The findings demonstrate that Canada’s multiculturalism and merit-based immigration systems fail to deliver equitable labour market outcomes. Integration is shown to be a dynamic, continuous negotiation rather than a linear process. The study advocates for more inclusive labour-market policies and organizational practices that recognize foreign qualifications, confront racial, gender, and religious bias, and strengthen support for economic integration in mid-sized Canadian cities like Saskatoon.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".