The Development of Conspiracy Theories During the Freedom Convoy
Bibliographic record
Abstract
Conspiracy theories have been prevalent throughout history, especially during periods of fear and uncertainty as people build a narrative against political elites and blame misdeeds on their malignant nature. A case study of this phenomenon can be examined in the Canadian Freedom Convoy. The Freedom Convoy began in early 2022 as a protest against COVID-19 mandates, attracting significant political attention as an unprecedented event that eventually forced Trudeau to invoke the Emergencies Act. As political tensions and opinions arose on social media, conspiracist groups began to develop conspiracy theories about the Truck Convoy in order to attract attention from potential allies/social groups and encourage commitment, coordination, and unity. We analyzed the frequency of political content from trending hashtags, as well as the frequency of conspiratorial dialogue. By understanding the distribution of such content, we can begin to understand how an ideology forms and fosters a community during times of crisis. Although it’s concluded from our research that conspiracy theories do not make a large portion of all political opinions regarding the Truck Convoy, the political opinions and conspiracy theories all express the same sentiments of being against political elites and wanting a dismantlement of the system. Furthermore, some conspiracy theories were expressed by those with incredibly large followings, thus increasing the spread of such conspiracies. It can be concluded that such conspiracy theories appeal to extreme political opinions that already distrust Trudeau, and both support a similar ideology and can thus develop a community against the current Canadian government.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".