Determinants of Immigrants' Decisions to Remain in Canada
Bibliographic record
Abstract
In recent years, Canada's immigration landscape has become complex and politically sensitive. Public discourse has increasingly portrayed immigrants as contributors to the housing and job shortage. In response, the Canadian government reduced the number of work permits and permanent residence invitations, focusing on attracting highly educated, English-proficient professionals with recognized work experience. These candidates are selected with the expectation that they will quickly integrate and effectively contribute to the economy. However, the reality for many newcomers is far more complicated. Despite meeting the selection criteria, skilled immigrants often encounter structural barriers, such as complex licensing processes, lack of Canadian credential recognition, and the persistent requirement for “Canadian experience.” These challenges prevent many from continuing their professional careers. In other words, immigrants arrive with the belief that their skills and qualifications are valuable but often find themselves undervalued and under-utilized. Meanwhile, the host society may view immigrants not as contributors powering social programs, but as burdens on an already strained system. Although considerable research has been done on the challenges immigrants face, relatively little is known about the immigrants’ motivations for choosing Canada in the first place and what might lead individuals to stay, despite not being able to work in the career they trained for in their country of origin. To improve retention and integration, it is essential to examine where mismatches between expectations and realities occur, how these disconnects influence long-term settlement decisions, what can be done to close the gap between immigration selection criteria and immigrant outcomes, and eventually, how Canada can send a clearer and realistic message to future newcomers and develop effective policies and supports to ensure their long-term success and contribution. Specifically, we want to examine: 1. What are the initial goals and expectations of immigrants coming to Canada, and how do these compare with their actual experiences after settling? 2. Does the degree of alignment between expectations and lived experience influence the intention/desire to stay in Canada long-term? 3. Are individuals higher in self-efficacy more likely to want to remain in Canada, and is an alignment between expectations and experience less determinant of their intention to remain?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.004 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.032 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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; both teacher heads agree on what is shown here.
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".