Polarizing figures of resistance during epidemics. A comparative frame analysis of the COVID-19 freedom convoy
Bibliographic record
Abstract
Using the 2022 Freedom Convoy in Canada as a case study, this article explores divisions between public understanding of resistance to health measures during epidemics and oppositional movements’ self-representations. It derives from a comparative analysis of five mainstream Canadian media (n = 516 publications) and of the Freedom Convoy’s Facebook page (n = 611 posts). The data were submitted to a rhetorical frame analysis and a thematic analysis, each factoring in a temporal dimension. Results show how this movement formed around a specific pandemic policy restrictions became increasingly understood from a binary logic concerning questions of identity. Protesters’ experience and analysis of COVID-19 mandates became peripheral in media and social media content, as the focus was instead placed on who was and was not acceptable in Canadian society. Results show that a dialogic relation between the frames used in Canadian mass media and in Facebook posts constructed increasingly dichotomous identities. The social implications of polarizing a political conflict around public health policies are discussed.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.021 | 0.032 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".