Stories of Resilience: How Small Immigrant Businesses in Toronto’s Suburbs Have Adapted in the Face of Covid-19 Pandemic
Bibliographic record
Abstract
Toronto is well-known for its multicultural diversity with half of the population born outside of Canada. This ethnocultural diversity is manifested in the establishments of immigrant businesses that have sprouted across the city, especially in its suburbs. These immigrant businesses promote economic vibrancy, enhance social interaction, and support community resilience at the neighbourhood level. However, small immigrant businesses continue to face greater barriers to market entry and entrepreneurial outcomes than their non-immigrant counterparts and are one of the most vulnerable sectors of the economy. The COVID-19 pandemic has further exposed and exacerbated their vulnerability. Immigrant entrepreneurship is a well-researched topic in the literature, but research on the effects of pandemics on small immigrant businesses is relatively limited. It is thus timely to investigate the barriers and challenges small immigrant businesses are dealing with in the face of the COVID-19 pandemic. Building upon the mixed-embeddedness theory, this paper addresses the following questions: How have small immigrant businesses adapted to the economic, political, and institutional contexts during the pandemic? What are the effective strategies that support the building of economic and community resilience? What are the implications for planning policies? Two suburban immigrant business areas in the City of Toronto were investigated through business surveys and key informant interviews. This paper found that the extent to which a small immigrant business was able to adapt their business to the pandemic environment and overcome barriers largely depended on (1) the existing local, co-ethnic, or family networks in their community; (2) the ethnic strategies used and the opportunity structure in which a business was embedded; and, (3) the formal and informal placemaking methods used by immigrant business owners for community building. It offers policy recommendations for supporting immigrant businesses and building community resilience
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".