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Record W2905481483 · doi:10.29173/af27263

Y a-t-il des traces de l'identité canadienne dans la BD québécoise?

2016· article· fr· W2905481483 on OpenAlexvenueaboutno aff
Mira Falardeau

Bibliographic record

VenueALTERNATIVE FRANCOPHONE · 2016
Typearticle
Languagefr
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

À travers six œuvres sélectionnées chez six grands artistes de la BD québécoise, choisis à chaque époque de 1900 à nos jours, cet article tente de répondre à cette question qui peut sembler ambigüe. Nous pourrons constater en regardant attentivement ces œuvres-phares qu’elles portent en elles cette double identité, québécoise et canadienne, qui les dynamise et les caractérise. Baptiste, le cocasse diplomate international d’Albéric Bourgeois (1876-1962), Onésime et les ruraux d’Albert Chartier(1912-2004), le super anti-héros loufoque Capitaine Kébec de Pierre Fournier (1949- ), le Carcajou fripon amérindien de Grozoeil (pseud. Christine Laniel (1951- ), la citadine solitaire du métro de Julie Doucet (1965- ), enfin Paul face à la société québécoise de Michel Rabagliati (1961- ), tous à leur façon sont les chantres d’une façon de voir qui nous réunit à travers nos divergences. Entre Baptiste, Onésime, Capitaine Kébec et Carcajou, il y a cette sorte d’humour complice qui est l’humour minoritaire. Quant aux alter egos de Doucet et de Rabagliati, à travers leurs fables autobiographiques, ils promènent leurs regards sur la double facette de la cité contemporaine, de l’anonymat à la solidarité, de la non-identité aux identités plurielles.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.050
Threshold uncertainty score0.366

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0170.011
Scholarly communication0.0080.003
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0200.001

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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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