Canada’s Response to International Travel During COVID-19 Pandemic – A Media Analysis 
Bibliographic record
Abstract
Abstract Background The media play a critical role in informing the public about the pandemic. International travel is a highly contested subject in the media during the COVID-19 pandemic at both the international and national levels. We examined Canadian media reporting on international travel restrictions during the pandemic, how these restrictions aligned with the IHR (2005), and how the narrative around international travel evolved for the pandemic.MethodsWe analysed Canada’s top three national newspapers by circulation, namely – The Globe and Mail, The National Post and The Toronto Star, published between Jan 1, 2020 - May 31, 2020. Our search yielded a total of 378 articles across the three newspapers. Upon removing duplicates and screening the remaining articles, we included a total of 62 articles for the analysis. We conducted a qualitative media content analysis by using the inductive coding approach.ResultsThree major themes were identified within the articles. These included: 1) The role of scientific and expert evidence in implementing travel restrictions; 2) Federal legislation, regulations and enforcement of international travel measures; and 3) Compliance with WHO guidelines in travel restriction policy- and decision-making. The federal government relied on scientific evidence for implementing international travel restrictions. The federal government fully exercised its powers under the Quarantine Act to enforce travel regulations and comply with the IHR (2005). The government embraced rules-based international order by following WHO recommendations on international travel, contributing to delaying border closure and travel restrictions until mid-March.Conclusion The media focussed significantly on international travel related issues during the early phase of pandemic. The dominant media narrative remained the need for earlier travel restrictions against international travel.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".