Entrepreneurship and self-employment among immigrant women in Canada: a narrative review toward an intersectional and intersectoral research agenda
Bibliographic record
Abstract
Purpose This article provides a narrative review of recent research on entrepreneurship and self-employment among immigrant women in Canada. The aim was to identify the state-of-the-art, current trends and findings in existing literature to suggest future research avenues. Design/methodology/approach Focusing on the Canadian context, a thorough search was conducted across four academic databases (EBSCO, ProQuest, Web of Science, and Cairn) for both peer-reviewed articles and grey literature. Fourteen papers and one report, published between 2010 and 2024, were selected and analyzed using a narrative approach. Findings The review identified the primary challenges faced by immigrant women entrepreneurs and self-employed workers in Canada according to recent research. Most of the existing literature comprises qualitative studies utilizing in-depth interviews, primarily focusing on specific ethnic groups. Research aimed at developing coordinated policy for support services constitutes a minority of the studies and are mostly situated in Quebec, highlighting a significant research gap for future investigation. Originality/value This paper consolidates the existing theoretical and empirical approaches to female immigrant entrepreneurship research within the Canadian context. The review sets the stage to suggest new avenues for further research, particularly to inform intersectoral planning and action to support immigrant women in entrepreneurial pursuits from an intersectionality standpoint.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".