Post-truth politics vs. newspaper coverage: the COVID-19 pandemic and transnational human migration in Canada, the United States and the United Kingdom
Bibliographic record
Abstract
Introduction In the context of a presumed post-truth world where emotion and ideology increasingly shape public discourse, this study explores the media’s role in constructing and disseminating narratives about transnational human migration. The COVID-19 pandemic added a new layer of complexity, biologically securitizing mobility at a time when migration was already framed by xenophobia and securitization in the Western world. This study investigates how major newspapers in Canada, the United States, and the United Kingdom covered migration during the first year of the pandemic, revealing how media constructions reflected and reinforced national attitudes. Methods A mixed-method discursive analysis was conducted on 225 articles (25 from each of nine major newspapers) from January 1 to December 31, 2020, selected for their topical relevance through the Factiva database. Articles were categorized quantitatively by topic and sentiment and qualitatively analyzed using constant comparison methods. The study focused on recurring topics, dominant narratives, and sentiment orientations (positive, neutral, neutral-negative, or negative) to identify country-specific media ‘truths’ about migration during the pandemic. Results Distinct national patterns emerged. Canadian newspapers exhibited largely positive or neutral portrayals of migrants, highlighting their economic and social contributions. U.S. coverage was polarized, reflecting both supportive and hostile attitudes, especially regarding Latin American migration and the Title 42 policy. U.K. articles were more frequently negative or securitized, especially concerning Channel crossings. Across all countries, themes of migrant precarity, humanitarian solidarity, and essential labor during COVID-19 were common, yet framed differently depending on national context and media sentiment. Discussion The findings illustrate how media in post-truth societies selectively constructed ‘truths’ about migration during a global crisis. While Canadian media maintained a largely humanitarian and pragmatic framing, U.S. and U.K. media leaned toward narratives of threat and economic burden. These constructions reflect deeper societal and political cleavages, where media act as both agents and arenas of discursive influence. The research underscores the importance of media literacy and critical discourse analysis in unpacking the role of journalism in shaping migration politics during times of uncertainty.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".