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Record W3215364666 · doi:10.22230/cjc.2021v46n4a3929

Enemy Imaginaries: A Case Study of the Far Right in Canada

2021· article· en· W3215364666 on OpenAlex

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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Communication · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCommunism, Protests, Social Movements
Canadian institutionsYork University
Fundersnot available
KeywordsAdversaryNationalismMulticulturalismPolitical scienceMedia studiesEvent (particle physics)Far rightSocial mediaSociologyGender studiesLawPoliticsComputer securityComputer science

Abstract

fetched live from OpenAlex

Background: Social media and digital technology play a central role in amplifying the potential harms of the far right. Analysis: The concept of enemy imaginaries is developed to map the digital and social media practices of far-right actors and groups in their antagonistic participation with and against a liberal, multicultural, globalist imagined community. Analysis focuses on a dramatic clash at a People’s Party of Canada event in Hamilton, Ontario, during the 2019 federal election. Conclusion and implications: Disparate far-right groups can momentarily crystallize around a particular event to define new nationalist objects that are symbolic of their networked and mediated fight against an imagined enemy.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.026
GPT teacher head0.294
Teacher spread0.268 · 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