Immigrant Entrepreneurship, Institutional Discrimination, and Implications for Public Policy: A Case Study in Toronto
Bibliographic record
Abstract
Immigration since World War 2 has been a primary engine of economic, social, and cultural change in Canada. Two of its important characteristics have been its ‘urban’ character and the non-European origins of immigrants since the 1960s. The Toronto Census Metropolitan Area (CMA) has been a major destination for those immigrants who have entered the self-employed sector of the economy in ever-larger numbers. The authors focus on the barriers and challenges experienced by the Polish, Portuguese, Caribbean, Korean, and Somali immigrants in the establishment and operation of their businesses in the Toronto CMA. With information collected through key-informant interviews, a questionnaire survey, and focus groups, it is found that, despite the Canadian commitment to multiculturalism at all levels of government, visible-minority entrepreneurs still confront more barriers in their business practice than do non-visible-minority entrepreneurs, with access to financing being a persistent problem. Given the increasingly multicultural nature of major Canadian cities and the acknowledged role of immigrants as an engine of economic growth, the authors identify barriers to entrepreneurship among immigrants as an area of clear concern both for policymakers and for scholars, and suggest solutions to address this concern.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.027 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".