Rolling towards Radicalization : Right-Wing Populist Identification on Social Media During the 2022 Canadian Truckers’ Convoy
Bibliographic record
Abstract
The scope of protests and political demonstrations have expanded beyond the physical environment, and are accessible through social media with live-streams, donation apps and comments. Social media facilitates remote participants to engage with those directly involved and on-site. This paper analyzes the case study of the 2022 Truckers’ convoy in Canada, and the effects of exposure to right-wing populist protest on political opinion. Social media was incorporated as a protest technique used throughout the Convoy occupation, used to amass widespread followings and online support (Zaiontz 2024). To investigate Convoy populism, I will use two large scale public opinion surveys of both Canadians and Albertans. In addition to survey data, content and keyword dictionary analysis are used to supplement the statistical models. Evidence is presented for the persuasive nature of social media Convoy discourses, where the findings suggest increased right-wing populist opinions among individuals on social media, with significant interaction effects in 2022.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".