Gender, Migrants and Entrepreneurship : Latin American Women Migrant Entrepreneurs in Ottawa
Bibliographic record
Abstract
Our research applies an intersectional lens to consider how gender, ethnicity and the minority status of immigrants, shape women migrant entrepreneurs’ motivations, measure of success, and barriers at different stages of the entrepreneurial journey, accounting for the sociocultural context. We applied a qualitative approach constructed around a case study strategy and semi-structured interviews with 6 Latin American migrant women entrepreneurs running a business in Ottawa, Canada. The results highlighted that there is wide range of motivations for women migrant entrepreneurs to start running a business and they are confronted with different barriers. They may share some similar motivations and barriers, but their socio-cultural context makes each of their situations unique. We came to the same conclusion regarding their definition of entrepreneurial success. This research brings a better understanding of the motivations and barriers faced by an understudied population and it also allowed them to define their own criteria for measuring success. The results are limited to only one study area and one ethnicity. A larger sample size with more cases from multiple study areas could provide further insights.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".