Supporting Canada’s COVID-19 resilience and recovery through robust immigration policy and programs
Bibliographic record
Abstract
Canada has been seen globally as a leader in immigration and integration policies and programs and as an attractive and welcoming country for immigrants, refugees, temporary foreign workers, and international students. The COVID-19 pandemic has revealed some of the strengths of Canada’s immigration system, as well as some of the fault lines that have been developing over the last few years. In this article we provide an overview of Canada’s immigration system prior to the pandemic, discuss the system’s weaknesses and vulnerabilities revealed by the pandemic, and explore a post-COVID-19 immigration vision. Over the next three years, the Government of Canada intends to bring over 1.2 million new permanent residents to Canada. In addition, Canada will continue to accept many international students, refugee claimants, and temporary foreign workers for temporary residence here. The importance of immigration for Canada will continue to grow and be an integral component of the country’s post-COVID-19 recovery. To succeed, it is essential to take stock, to re-evaluate Canada’s immigration and integration policies and programs, and to expand Canada’s global leadership in this area. The authors offer insights and over 80 recommendations to reinvigorate and optimize Canada’s immigration program over the next decade and beyond.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.009 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".