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Truck Fudeau: Algorithms, Conspiracy and Radicalization

2023· article· en· W4387168314 on OpenAlex
Michael Hoechsmann, Miranda McKee

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueNorteamérica · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsYork UniversityLakehead University
FundersMinisterio de Ciencia, Innovación y Universidades
KeywordsRhetoricPopulismBattleMedia studiesIdeologyRadicalizationSocial mediaMainstreamMandatePolitical sciencePublic opinionLawSociologyTerrorismPoliticsCriminologyHistory

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.616
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.351
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it