Conspiracy theory, anti-globalism, and the Freedom Convoy: The Great Reset and conspiracist delegitimation
Bibliographic record
Abstract
Abstract In this article, we analyse how anti-globalist conspiracy theories were mobilised online to delegitimise national authorities and policies designed to curb the Covid-19 pandemic in Canada. These conspiracy theories attacked the political authority underpinning public health measures and targeted purportedly ‘liberal’ policies and ‘globalist’ actors. Our case study examines the Freedom Convoy, a series of protests against Covid-19 vaccine mandates that began in Canada but inspired global demonstrations. The Freedom Convoy fostered and relied upon anti-globalist conspiracy theories, including the ‘Great Reset’ and ‘Great Replacement’, both of which posit a global conspiracy to erode national sovereignty and impose a ‘liberal’ international order. We investigate far-right social media commentary from 4chan’s Politically Incorrect imageboard /pol/, Infowars, and Rebel News, showing how conspiratorial claims were marshalled in alt-tech spaces. These narratives were used to delegitimise public health measures to combat Covid-19 and the Liberal Trudeau government by linking them to various ‘globalist’ forces. In exploring three mechanisms of delegitimation – externalisation, personification, and Othering – we argue that far-right movements like the Freedom Convoy, motivated by anti-globalist conspiracism, mobilise the international realm by leveraging the legitimacy gap of international organisations and agendas to undermine the political authority of actors at the national level.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.090 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".