Supporting Canada’s COVID-19 resilience and recovery through robust immigration policy and programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it