The experiences of immigrant entrepreneurs in a medium‐sized Canadian city: The case of St. John's, Newfoundland and Labrador, Canada
Bibliographic record
Abstract
There is growing interest in the dynamics of immigrant entrepreneurship in non‐traditional immigrant gateway cities in Canada. Encouraging immigrant businesses is a particularly pressing imperative for Atlantic Canada's small and medium‐sized cities, which struggle with aging labour markets, youth out‐migration, and difficulty attracting and retaining newcomers. Recent research on immigrant entrepreneurship highlights the geography of entrepreneurial benefits and challenges across Canadian cities. Our study contributes to this field by examining the experiences of immigrant entrepreneurs on the “edge”—working in Canada's easternmost city, St. John's, Newfoundland and Labrador. Our findings are based on 28 interviews conducted with immigrant entrepreneurs and key informants in St. John's. As a remote, peripheral city within Canada with a small immigrant population and an economy intimately tied to booms and busts in global oil markets, we found that St. John's presents a distinct set of challenges for immigrant entrepreneurs. Yet, a recent rush to encourage a start‐up ecosystem—through a university‐based incubator program and new provincial nominee streams—is also creating new opportunities for self‐employment and shifting the terrain of support towards white‐collar businesses. Ultimately, this study highlights the variegated experiences of a diverse set of immigrant entrepreneurs in St. John's.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.042 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".