Immigrants Doing Business in a Mid‐sized Canadian City: Challenges, Opportunities, and Local Strategies in Kelowna, British Columbia
Bibliographic record
Abstract
Abstract Lacking a tradition of settling immigrants and the appropriate infrastructure to integrate them, small‐ and medium‐sized cities often face the challenge of attracting and retaining immigrants. Using a mixed methods approach, this study compares the experiences of immigrant and non‐immigrant entrepreneurs in a mid‐sized Canadian city, Kelowna, British Columbia. A survey reveals different experiences between these two groups, with immigrants facing more challenges. In the absence of institutionally complete communities or strong ethnic economies, immigrants do not rely extensively on their own community resources, an element considered instrumental for immigrant business development in large cities. Compared to non‐immigrants, immigrant entrepreneurs have a more optimistic outlook on doing business in Kelowna; this is encouraging for a city trying hard to attract immigrant investment. Key informants recommended transforming the city into a more welcoming community, establishing appropriate support infrastructure, and removing potential institutional offsets. This paper adds new theoretical insights to the literature on immigrant entrepreneurship; all socio‐cultural, political‐institutional, and economic‐structural considerations are embedded in geography. The findings also have implications for growth strategies in small‐ and medium‐sized cities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".