A COVID-19 State of Exception and the Bordering of Canada’s Immigration System: Assessing the Uneven Impacts on Refugees, Asylum Seekers and Migrant Workers
Bibliographic record
Abstract
Responses to COVID-19 have been characterized by rapid border closures that have transformed the pandemic from a crisis of health to a crisis of mobility. While Canada was quick to implement border restrictions for non-citizens like refugees and asylum seekers, exemptions were made for some migrant groups like temporary workers. The pandemic marked a departure from who is considered worthy of admission to Canada. In fact, the border through restricted and securitized measures has filtered desirable versus non-desirable migrants, creating a hierarchy among migrants within Canada’s immigration system by categorizing groups into those deserving versus non-deserving of admission. Deeply embedded societal discrimination and structural inequalities means that COVID-19 has exacerbated the vulnerabilities of migrant groups more than others. COVID-19 has placed an uneven burden on refugees who face increased border restrictions, significant health and safety risks, and limitations in accessing human rights. This paper documents the challenges, social and economic impacts, and exacerbated vulnerabilities border closures have imposed on refugees, asylum seekers and temporary migrants. We assess the many challenges that COVID-19 has created at the intersection of border studies, security resilience and human rights. We employ the conceptual frame of security resilience to critically analyse the dynamics of how and why border strategies have restricted migrant groups in times of crisis and amounted to an unjustified weakening of refugee rights. Finally, we argue that social resilience, which is rooted in rights-based strategies, not only ensures that societies are prepared to meet external shocks and disruptions, but that policy responses mitigate societal discrimination and inequalities. We highlight these strategies as effective mechanisms for reconciling both public health concerns and the rights of migrants to create more cohesive societies in times of crisis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".