Bibliographic record
Abstract
How often do we have or take the opportunity to ask people who have immigrated to Canada about their journeys? What could we learn from their stories? Canada is a country built upon immigration. Today, Canada is one of a handful of countries that actively recruits and promotes immigration as a means to sustain population growth and respond to internal labour force needs. Since 1967, immigration policy changes widened the range of sending countries to include countries located in the Global South and increasingly immigrants are well-educated and highly skilled people from urban settings. This study took place in the third largest immigration destination in Canada, Montreal. It focussed on the experiences of a group of fourteen women. Sooner or later, all of these women turned to entrepreneurship to generate economic activities. By contextualizing their experiences, I hoped to gain a better understanding of the opportunities and challenges they faced in accessing economic opportunities in Canada. Generally, past research has focussed upon male dominant models and often overlooked the experiences of immigrant women entrepreneurs. It is important to focus on women in particular to better understand the gendered implications of entrepreneurship and to offer different experiences that work against stereotyping all entrepreneurs into one homogenous category. By studying the specific experiences of these fourteen women, and by using a qualitative approach, I hope to illustrate the importance of textualized research in building a better understanding about immigrant women entrepreneurs in Canada.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".