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Record W4283768947 · doi:10.4000/communication.15199

Endoctriner et radicaliser sur la Toile

2022· article· fr· W4283768947 on OpenAlexaffvenue
Mathieu Colin, Solange Lefebvre, Dianne Casoni

Bibliographic record

VenueCommunication · 2022
Typearticle
Languagefr
FieldArts and Humanities
TopicMarxism and Critical Theory
Canadian institutionsUniversité de MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsArt

Abstract

fetched live from OpenAlex

Cet article s’appuie sur les résultats d’une recherche portant sur l’extrême-droite et qui visait à analyser les liens multiples existant entre les médias et la radicalisation menant à la violence. L’équipe de recherche a recouru à plusieurs types de méthodologies aussi bien quantitatives que qualitatives découlant d’entretiens menés avec près de 70 individus radicalisés, et d’observations directes d’événements réunissant leurs groupes Le présent article retient les propos explicites de deux individus radicalisés et s’attèle en particulier à l’analyse de centaines de sites consultés sur une période de plusieurs mois, entre 2018 et 2020

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)0.003
metaresearch head score (Gemma)0.008
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 score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.014
Scholarly communication0.0070.004
Open science0.0010.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.002

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 head0.028
GPT teacher head0.262
Teacher spread0.233 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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