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Record W4312090026 · doi:10.3138/cjc.2022-0021

Is the Alt-Right Popular in Canada? Image Sharing, Popular Culture, and Social Media

2022· article· en· W4312090026 on OpenAlexaffvenueabout
Fenwick McKelvey, Scott DeJong, Saskia Kowalchuck, Elsa Donovan

Bibliographic record

VenueCanadian Journal of Communication · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsConcordia University
Fundersnot available
KeywordsPopularityPopular cultureSocial mediaPoliticsAnalyticsSociologyPolitical sciencePublic relationsMedia studiesInternet privacyComputer scienceData scienceLaw

Abstract

fetched live from OpenAlex

Background: In popular coverage and social media analysis, the alt-right has been described as a popular phenomenon. Following Stuart Hall’s understanding of popular culture, we question the status of the alt-right in Canada as both a political and methodological problem that requires critical attention to social media metrics and critical experimentation in developing new digital methods. Analysis: Our study developed a novel method to analyse image circulation across major social media platforms. We find that image sharing is marginal, yet the spread of images distinguishes political communities between Twitter hashtags, subreddits, and Facebook pages. We found a distinct alt-right community in our sample, active but isolated from other popular sites. Conclusion and Implications: While the findings suggest the limited significance of image sharing to conceptualize popularity in cross-platform analysis, our novel method offers a compelling alternative to corporate social media analytics and raises new questions about how popular politics, especially the popularity of the alt-right, may be studied in the future.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.294

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0110.005
Scholarly communication0.0070.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.032
GPT teacher head0.291
Teacher spread0.259 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2022
Admission routes3
Has abstractyes

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