A Saskatchewan Case Study of Canada’s Federal and Provincial Immigrant Entrepreneur Immigration Policies
Bibliographic record
Abstract
Immigration continues to be a significant factor in the demographic and economic growth of Canada. Canadian provinces and territories work hard to attract and retain skilled newcomers to their cities and communities to participate in the labour market by filling job vacancies and investing into new and existing business in sectors and communities. Skilled immigrants are responsible for a significant portion of the overall population job growth in the Canadian economy and play an outsized role in entrepreneurship in communities across the country. They establish businesses that serve the distinct and growing needs of Canada’s expanding cultural communities, purchase existing businesses from Canada’s aging and retiring entrepreneur class, invest in and operate innovative firms in such diverse fields as new communications technology to agri-business. Despite the strategic importance of immigration in the demographic and population growth of Canada and the significant role immigrant entrepreneurs play in the establishment and ongoing operation of businesses, there is little analysis of the public policy mechanisms that work to attract and retain skilled immigrant entrepreneurs. Using the province of Saskatchewan as a case study, this research provides an historic overview of the existing public policy mechanisms created to attract skilled immigrant entrepreneurs to Canada and more effectively distribute them to provinces, territories, and communities of all sizes across the country. It also explores why there has been little academic scrutiny of immigration policy related to immigrant entrepreneurship, the federal and provincial policy mechanisms in place to facilitate these activities, and the outcomes of these processes.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.035 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".