Entrepreneurial Intention of Brazilian Immigrants in Canada
Bibliographic record
Abstract
ABSTRACT This study provides evidence of possible sociodemographic characteristics that would influence the intention of Brazilian immigrants to engage in ventures in Canada. Data were collected through surveys released on Brazilian Facebook groups. A total of 675 Brazilian respondents living in Canada were triangulated with data from seven semi-structured interviews conducted in Canada and with two consulate officials. Survey data analysis was performed with logit equations to check relationships between entrepreneurial intention (EI) and variables - namely, gender, age upon arrival, level of education, length of stay in the country, student/work/tourist visa status upon arrival, and citizenship application status/permanent migration. The key results point to factors with a positive influence on the intention to venture: gender (being female) and all visa status and other variables were either non-significant or had a negative influence. Of the entrepreneurs, age upon arrival was a significant predictor. Variables such as level of education, time in the country, and tourist visa had a negative influence. This paper contributes theoretically by evidencing recent immigration patterns and variables related to entrepreneurial venturing in the Brazilian immigrant community in Canada, which may support mechanisms for attracting and fostering future entrepreneurs. Further comparative studies between other Brazilian and ethnic communities are proposed, including other variables.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".