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Record W4322580289 · doi:10.1177/20563051231157604

The Use of TikTok for Political Campaigning in Canada: The Case of Jagmeet Singh

2023· article· en· W4322580289 on OpenAlex
Aidan Moir

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

VenueSocial Media + Society · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsPopulismPoliticsAppealSocial mediaContext (archaeology)DemocracySociologyMedia studiesPolitical scienceUndue influenceLawHistory

Abstract

fetched live from OpenAlex

TikTok is a critical platform for political campaigns seeking to engage with new publics through digital advocacy. Jagmeet Singh, the leader of Canada’s New Democratic Party, has emerged as a TikTok celebrity since establishing his profile in 2019. At the time of writing, he is the only Canadian federal party leader using TikTok with his interactions greatly surpassing those on his other social media profiles. Strategically utilizing TikTok to promote his social justice-oriented political platform and to build momentum in preparation for a snap election, his digital campaign has received extensive attention from the Canadian press. Through qualitative content analysis of his videos and news media coverage of Singh’s activity on TikTok, this article questions how his TikTok profile thematically engages with social democratic politics within the context of the permanent campaign. Attention is directed toward how Singh employs TikTok’s features to establish his brand of left-wing populism and advocate against systematic social inequality to appeal to TikTok’s youthful demographic.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.125
GPT teacher head0.333
Teacher spread0.208 · 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