#AFRINGEMINORITY: TIKTOK’S PLATFORM VERNACULAR AND FRAMING COLLECTIVE IDENTITY IN THE FREEDOM CONVOY PROTESTS*
Bibliographic record
Abstract
This study uses a qualitative thematic analysis drawn from 813 TikTok videos to examine how TikTok’s platform vernacular shapes the expression of collective identity vis-à-vis collective action frames. This work explores the contentious case of the Canadian far-right freedom convoy protests between January 29 and February 21, 2022. This study finds that freedom convoy supporters’ framing of collective identity relied upon antipathy toward state intervention and nationalistic pride that desired a return to “normal,” moralized in the name of “freedom.” Additionally, this work finds that this identity work was augmented through three key vernacular practices: alternative broadcasting, monologuing, and audio memes. These vernacular practices shaped supporters’ identity work, instructing how the movement was collectively rendered on TikTok. Overall, this work contributes to our understanding of how social movements’ identity work practices are inextricably bounded through TikTok’s unique communicative culture.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".