Truck Fudeau: Algorithms, Conspiracy and Radicalization
Bibliographic record
Abstract
COVID-19 public health mandates used in Canada and elsewhere proved to be potent measures for radicalizing new groups to right-wing ideas and gatherings, as well as for broadly main-streaming anti-government and anti-media rhetoric. This is visible online on the sites of some influencers who have waged a battle against COVID-19 mandates, and in real world protests such as Canada’s Freedom Convoy, an event that culminated in a three-week occupation of Canada’s capital, Ottawa, from January 29 through February 20, 2022. The movement had some appeal beyond its core groups and picked up momentum as time went on. The rise of right-wing populism in Canada is a result of multiple factors, but in this article, we will limit the purview to how an anti-vax and anti-mandate movement served to radicalize newcomers to a position antithetical to that of public health authorities and mainstream opinion, and also how this ideological struggle was mobilized and received via algorithm-driven online media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".