Petro-masculine Soundscapes: Music, Sound, and Violence at the 2022 Ottawa “Freedom Convoy”
Bibliographic record
Abstract
In January 2022, a series of truck convoys organized by right-wing activists converged on Parliament Hill in Ottawa, Canada’s national capital, demanding that the federal government end all vaccine mandates and public health protocols related to COVID-19. While never a unified movement, opposition to pandemic-related mandates became a rallying cry that brought together an assortment of right-wing groups. Coalescing around ill-defined notions of freedom, the self-proclaimed “Freedom Convoy” occupied ten square blocks in downtown Ottawa for nearly four weeks. The sound of trucks honking, engines revving, air horns, train horns, and amplified music put many residents under extreme duress, yet also drew people to the area. Music and sound were key characteristics of the occupation. The Convoy’s mainstage became a platform for a wide range of amateur singers, songwriters, rappers, and DJs, and supporters pointed to dance parties in the streets as evidence of the occupation’s peacefulness and popularity. The constellation of beliefs, behaviours, and attitudes that produced the Convoy’s thunderous soundscape is readily captured in Cara Daggett’s concept of petro-masculinity. Petro-masculinity articulates the historic relationship between fossil fuels and white heteropatriarchy, and describes a hypermasculine identity based in racism, misogyny and climate denial that has become prevalent in right-wing movements in North America and elsewhere. This paper examines how music was used to provide a veneer of multiculturalism and widespread support, playing into nationalist myths of Canadian liberalism and tolerance while evincing a kind of cultural extractivism that resonates with the logics of fossil fuel capitalism and settler colonialism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.041 | 0.021 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".