“Guided by Science and Evidence”? The Politics of Border Management in Canada's Response to the COVID-19 Pandemic
Bibliographic record
Abstract
The limited and coordinated use of travel measures to control the international spread of disease, based on scientific evidence and respect for human rights, are core tenets of the World Health Organization's (WHO) International Health Regulations (IHR). Yet, during the COVID-19 pandemic, there has been near universal and largely uncoordinated use of travel measures by national governments, characterized by wide variation in what measures have been used, when and how they have been applied, and whom they have been applicable to. Given the significant social and economic impacts caused by travel measures, analyses to date have sought to understand the effectiveness of specific measures, in reducing importation and onward spread of SARS-CoV-2, or needed efforts to strengthen compliance with the IHR. There has been limited study of the role of national-level policy making to explain these widely varying practices. Applying path dependency theory to Canadian policies on travel measures, this paper analyses the interaction between science and politics during four key periods of the pandemic response. Bringing together systematic reviews of the scientific literature with parliamentary records, we argue that the evidentiary gap on travel measures during the initial pandemic wave was filled by political and economic influences that shaped when, how and for whom testing and quarantine measures for travelers were applied. These influences then created a degree of path dependency that limited the capacity of government officials to change policy during subsequent waves of the pandemic. This was accompanied by frequent government claims of reliance on science and evidence but limited transparency about what and how scientific evidence informed policy decisions. We argue that, over time, this further politicized the issue of travel measures and undermined public trust. We conclude that fuller understanding of the interaction between science and politics in national decision-making about border management during the COVID-19 pandemic is essential to future efforts to strengthen international coordination under the IHR.
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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 | 0.137 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.014 | 0.030 |
| Scholarly communication | 0.028 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".