Exploring The Entrepreneurial Experience Of African Immigrant Women In Canada: Sub-Saharan Africa Perspective
Bibliographic record
Abstract
Social, political, and academic activeness in women entrepreneurship has increased over the years. This research project examines African immigrant women entrepreneurs' real-world experiences from a pragmatist perspective embedded with transcendental phenomenology, using qualitative methods in multiple case studies. The study examines 12 Sub-Saharan Africa immigrant women's entrepreneurial experiences in Canada, predominantly West Africans, to investigate how these women approach entrepreneurial activity in Canada, and what has influenced their choices? This study’s findings illuminate the women's experiences through rich analysis, which may influence future policy. The results identify specific systemic environmental and social challenges faced by AIWEs in Canada and reveal that their choices of types of business are often characterized by perseverance. This study found that Canada is proud to serve as a multicultural nation that believes in diversity and equal opportunity. However, systemic marginalization and inconspicuous racism exist—affecting the entrepreneurial ecosystem that inhibits Sub-Saharan AIWEs to exhibit their full potential in their entrepreneurial endeavor. KEYWORDS AND ABBREVIATION: Women entrepreneurs, immigrants, African. African immigrant women entrepreneurs (AIWE)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".