Borders, Boundaries, and the Impact of COVID-19 on Immigration to Canada (Editors' Introduction)
Bibliographic record
Abstract
Although Canada has long been regarded as a global leader in immigration policies and practices (Esipova et al., 2020), the COVID-19 pandemic revealed and exacerbated existing challenges and vulnerabilities in Canada's immigration system (Esses et al., 2021).Border closures restricted who was able to travel, favoring temporary foreign workers who were deemed essential, while also creating lost cohorts of international students (Brunner, this issue) and weakening refugee rights (Abu Alrob & Shields, this issue).Considering employment specifically, the COVID-19 pandemic created and amplified disparities between workers who are required to physically attend work versus those who can work remotely, and it highlighted the issue of who has access to safe working conditions, paid sick leave, alternative childcare options, and other work-related resources and benefits (Dobusch & Kreissl, 2020).As Brunner (this issue) explains, highly uneven (im)mobility has been illuminated throughout the COVID-19 pandemic.Between nations, border closures and restrictions were applied unevenly, impacting who was permitted to travel.Canadian borders closed in March 2020 to all categories of immigrants (economic, family, refugees and protected persons), resulting in a significant reduction in admissions to only 184,370, 46% short of the targeted 341,000 for that year (El-Assal, 2021).In the midst of these border closures, certain Temporary Foreign Workers (TFWs) were deemed
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".