Balancing Immigration Disparities in Canada
Bibliographic record
Abstract
Canada’s immigration system is globally recognized for its merit-based approach and commitment to multiculturalism; however, recent trends reveal a growing imbalance in permanent resident visa allocations by country of origin, particularly the overrepresentation of Indian nationals compared to other major source countries like the Philippines, China, and Nigeria. This capstone project investigates the extent and causes of these disparities. It analyzes immigration data from 2015 to 2024 in conjunction with Canadian policy shifts, including changes to Express Entry, the Post-Graduation Work Permit (PGWP), and the Comprehensive Ranking System (CRS).Using a mixed-methods approach focused on both quantitative data and policy analysis, this study compares the impact of Canadian immigration policies across the top four source countries for immigration to Canada. Immigration, Refugees and Citizenship Canada (IRCC) and Statistics Canada data were used to examine trends by immigration stream, including economic, family, and refugee streams, while also considering demographic indicators such as language proficiency and education levels. The findings suggest that the permanent resident intake spikes align with policy changes to immigration pathways like Express Entry, the PNP, and the PGWP which prioritize candidates who have high proficiency in English, Canadian work or study experience, and recognized credentials. These characteristics are disproportionately common among applicants from India, many of whom arrive in Canada as international students or skilled workers. Subsequently, they often score highly in the CRS and, as such, are more likely to be selected for permanent residency compared to applicants from countries with less access to Canadian education or English language classes.External factors, such as U.S. visa restrictions and changing socioeconomic conditions in source countries, were also assessed but found to be insufficient to explain the disparity. To promote equity within the system and maintain Canada’s multiculturalism objectives, this paper proposes policy reforms such as federally managed per-country caps on immigration and modification of the CRS to ensure more equitable access for applicants from countries with diverse educational systems. The project concludes that without intervention, these structural biases risk undermining Canada’s diversity goals and the legitimacy of its immigration system.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".