#AFringeMinority: Collective identity within the 2022 Freedom Convoy protests on social media
Bibliographic record
Abstract
In early 2022, Canada – and the world – was gripped by protestors’ disruptive occupation of Ottawa’s downtown core, and several Canada-U.S. border crossings. Operating under the banner of the “Freedom Convoy,” an amalgamation of far-right actors – from anti-government extremists, White nationalists, and anti- “vaxxers” – united against Canada’s COVID-19 public health mandates. Instructive to understanding the Freedom Convoy’s ascendance was their ability to construct a symbolically inclusive collective identity, and their use of social media to that end. However, there are three notable gaps hindering a fuller understanding of these processes: how far-right social movements use short-form video platforms, like the pandemic favourite, TikTok, to mediate their collective identity, and how protestors’ TikTok use for identity building compares cross-nationally, and across platforms. I aimed to remedy these gaps, making decisive contributions to our understanding of collective identity’s mediation on social media. First, I explore Canadian Freedom Convoy supporters’ use of TikTok for expressing collective identity frames. I uncovered that the movement’s anti-institutional collective identity was buoyed by TikTok users’ practices, including alternative broadcasting, monologuing, and audio memes. I argue that TikTok’s emphasis on visuality, personability, and imitability was beneficial to conveying the Freedom Convoy’s anti-institutional collective identity. Second, I investigated the Freedom Convoy cross-nationally, looking into Canadian and New Zealanders’ use of TikTok to communicate collective identity frames. I found that New Zealanders’ collective identity did not passively copy Canadians’ anti-institutional collective identity, but rather “re-contextualized” these frames within their own localized political struggles, national histories, and symbolic registers. Moreover, I found that New Zealanders’ TikTok practices subtly differed from their Canadian counterparts, working to better capture New Zealand’s socio-cultural specificities, and the forceful crackdown on protests in Wellington. Lastly, I examined Canadian Freedom Convoy supporters’ use of TikTok and Facebook to articulate collective identity frames. I found that, while certain practices transcended platforms’ borders (e.g., alternative broadcasting, monologuing), they were nevertheless mediated through platforms’ specificities. I also found evidence that other practices were more recalcitrant to being transmuted (e.g., audio memes, public relations messaging), owing to each platform’s material infrastructure, and culture of use.
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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 | 0.002 | 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 | 0.024 | 0.016 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".