The Canadian experience from the perspective of Brazilian immigrant entrepreneurship in Toronto
Bibliographic record
Abstract
Objective: The study aims at analyzing the socio-demographic profile of Brazilian immigrant entrepreneurship in Toronto, its entrepreneurial behavioral traits, in addition to their business profiles. Methodology/approach: This research is multi-method, with qualitative predominance, being exploratory-descriptive. For data-collection it was conducted a survey and further deepening with face-to-face interviews and field observations. Main results: The majority declared themselves to be from the Brazilian Southeast, white, 35 to 49 years old, married, with children, high academic and professional background. The influence of the state of social malaise in Brazil and the official Canadian discourse, seem to act as factors of "expulsion-attraction" to migration. The job condition of unemployment upon immigrants’ arrival can “push” them into necessity-driven entrepreneurship, although there are also ventures that have identified opportunities. Most of the businesses are small and operate in the service sector, concentrated on the West End of Toronto. In many businesses, there was a strong search for identification with the ethnic community itself, which suggests the formation of 'enclave economy', but in some cases, the main market for local consumers was targeted. Theoretical/methodological contributions: The article highlights the importance of conducting a multi-method research to understand possible entrepreneurial configurations by Brazilian immigrants. Relevance / originality: the article has academic relevance given the scarce work on the theme of Brazilian immigrant entrepreneurship overseas. Truly little is known about this phenomenon in Canada. Social and management contributions: The entrepreneurial trajectories described minimize risks for future immigrants; in addition, the discussion about the social capital of the ethnic community allows comparisons with the business of Brazilians in other countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".