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Record W2809234752 · doi:10.7202/1047977ar

Discours racistes et propagande haineuse. Trois groupes populistes identitaires au Québec

2018· article· fr· W2809234752 on OpenAlexaffvenueabout
Maryse Potvin

Bibliographic record

VenueDiversité urbaine · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Cet article exploratoire vise à cerner les éléments de « propagande haineuse » utilisés par trois groupes populistes identitaires (Atalante, La Meute, la Fédération des Québécois de souche) sur leurs pages Facebook publiques entre le 29 janvier et le 15 mars 2017, après l’attentat du 29 janvier à la grande mosquée de Québec. L’article définit les groupes populistes de type identitaire au Québec et rappelle les principaux mécanismes du racisme, les procédés classiques utilisés par les médias traditionnels qui permettent d’influencer les idées et les représentations du public (agenda setting,framing,priming), les techniques classiques de propagande et les différentes balises juridiques (pénales et civiles) qui encadrent la propagande haineuse au Canada. L’article montre comment les leaders d’opinion et/ou administrateurs de ces pages Facebook publiques en font un usage stratégique (pour alimenter le néonationalisme, orienter les échanges sur des enjeux identitaires et maintenir un sentiment de crise et d’appartenance commune) et dont les effets préjudiciables sont visibles.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.085
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0120.005
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.022
GPT teacher head0.302
Teacher spread0.280 · 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 designQualitative
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

Citations14
Published2018
Admission routes3
Has abstractyes

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