The polarized “naturalizations” of the 2022 Freedom Convoy
Bibliographic record
Abstract
During the month-long “Freedom Convoy” protest in Ottawa (Canada), protesters were ascribed many attributes (violent, extremist, hateful, disinformed) which they refuted. Protest organizers insisted the Freedom Convoy was peaceful, loving, and included “average Canadian citizens fighting for freedom”. This research is interested in the construction of this representational divide and its consequences. It analyzes the polarized social representations of the COVID-19 Freedom Convoy by using social representation theory, and more specifically, Negura and Plante’s model of “naturalization”. News articles ( n = 516) from Canadian media and Freedom Convoy organizers’ Facebook posts ( n = 611) were submitted to a rhetorical frame analysis. Results show how communications from organizers and the media both contributed to the naturalization of conflicting representations by (1) associating the movement with a desirable/undesirable identity, (2) neglecting nuanced perspectives, (3) instrumentalizing their representation to justify the legitimacy/illegitimacy of the protest, and (4) validating the representation by focusing on incidents that ratified the Freedom Convoy’s “goodness” or “badness”. We argue that this single protest became two opposed and morally charged “objects” impossible to reconcile, which prevented dialogue. Social implications of polarized naturalizations during epidemics are discussed.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".