Bibliographic record
Abstract
Nearly a year after the start of Canada’s 2022 Freedom Convoy—a series of protests and blockades that brought together a wide variety of far-right activists and extremists, as well as ordinary Canadians who found common ground with the aggrieved message of the organizers—the question of whether and to what degree foreign actors were involved remains largely unanswered. This paper attempts to answer some of those questions by providing a brief but targeted analysis of Russia’s involvement in the Freedom Convoy via media and social media. The analysis examines Russian involvement in the convoy through the lenses of overt state media coverage, state-affiliated proxy websites, and overlap between Russian propaganda and convoy content on social media. The findings reveal that the Russian state media outlet RT covered the Freedom Convoy far more than any other international media outlet, suggesting strong interest in the far-right Canadian protest movement on the part of the Russian state. State-affiliated proxy websites and content on the messaging platform Telegram provide further evidence of Russia’s strategic interest in the Freedom Convoy. Based on these findings, it is reasonable to infer that there was Russian involvement in the 2022 truck convoy, though the scope and impact remain to be determined. Received: 2023-01-13Revised: 2023-01-24
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".