Replication Data for: Transnationalism and Populist Networks in a Digital Era: Canada and the Freedom Convoy
Bibliographic record
Abstract
The growth and success of right-wing populist movements globally has been remarkable since the early 2010s. Indeed, populist parties in Europe, Asia, Latin America, and North America have received tremendous electoral success, shaping a movement for the people and by the people within the political sphere. To what extent do populist movements influence other such programs across national borders? Research has suggested that globalization has facilitated the spread of populist ideology. Transnational populism emphasizes the “people” as a “horizontal, membership-based collective with membership premised on an in/out logic between nations, allowing populist national movements to engage and share a global ideological program. This paper seeks to understand and measure to what extent populism has become a transnational movement and identify how populism moves across national borders through online political participation. To explore this question, we collected over 6.7 million digital trace data on X/Twitter during Canada’s January–February 2022 Freedom Convoy movement. Receiving support from thousands of citizens, the Freedom Convoy revealed the ability of populist ideology to move aimlessly across international borders. We used a deep-learning model applied to text analysis to implement a classification task to measure populist narratives during the movement.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.032 |
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".