Community Resources and Opportunities in Ethnic Economies: A Case Study of Portuguese and Black Entrepreneurs in Toronto
Bibliographic record
Abstract
Relatively few attempts have been made by geographers in Canada to study the structure and development of ethnic entrepreneurship among immigrant groups, and particularly among visible minorities. The purpose of this study is to examine the behaviour, strategies and barriers faced by owners of ethnic businesses in order to evaluate how race and ethnicity impact upon entrepreneurship. In particular, the study aims at investigating whether intergroup differences exist with respect to the utilisation of group resources (such as family, friends, and community support/ties) and how these resources contribute to the formation, maintenance and success of Portuguese- and Black-owned businesses. Data were obtained from a questionnaire survey that was administered to Portuguese and Black entrepreneurs in the Toronto CMA. The evidence indicates that Portuguese differ significantly from Black entrepreneurs in that they rely more often on their community ('ethnic') resources. However, Black entrepreneurs encountered more barriers in starting and/or operating their current business, particularly in obtaining credit/loans from financial institutions and banks. Nonetheless, despite such barriers, Black entrepreneurs are more optimistic than the Portuguese with respect to the future of their businesses. The 'demographic revolution' that is taking place in Canada, and particularly in Toronto—with the arrival of important contingents of visible minorities—is pointed to by Black entrepreneurs as one of the major reasons for their optimism regarding the growth of Black entrepreneurship in Toronto.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".