MétaCan
Menu
Back to cohort
Record W2296651558 · doi:10.1353/crc.2016.0001

Paul à Québec , le génie des lieux comme patrimoine identitaire

2016· article· fr· W2296651558 on OpenAlexaffvenueabout
Sylvie Dardaillon, Christophe Meunier

Bibliographic record

VenueCanadian review of comparative literature · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsCanadian Heritage
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Depuis bientôt quinze ans, le dessinateur québécois Michel Rabagliati fait de sa propre vie le terreau des aventures de Paul, son alter-ego. Paul est un « héros ordinaire ». Sa vie est celle de tout-un-chacun, avec ses joies, ses peines. En 2009, l " auteur remportait le prix du Jury du Festival d " Angoulême avec son album Paul à Québec. Graphic novel de 187 pages, la bande dessinée est centrée sur le beau-père de Paul, Roland Beaulieu. Ce dernier, atteint d " un cancer généralisé, vit une véritable descente aux enfers. Rien n " est épargné aux lecteurs depuis les premiers symptômes jusqu " à la phase terminale. C " est l " occasion pour Rabagliati de rendre hommage à son propre beau-père qu " il avait en admiration. Roland est un Québécois « pure souche », né près des remparts de la « Vieille capitale », un self-made man exemplaire. C " est cet album que nous avons choisi de privilégier, afin d " analyser comment Rabagliati parle, à sa façon, de l " identité québécoise. En 2009, Michel Labrie, dans Le Mouton Noir, journal en ligne québécois, voyait en Paul le témoin d " une « identité québécoise » qu " il définissait de la manière suivante : Paul témoigne de l " identité québécoise. Il la décline par une juste et fine mise en scène des valeurs qui sont les exigences mêmes de notre dignité : l " amour et l " amitié, la famille et le travail.. 1

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.291
Teacher spread0.258 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueCanadian review of comparative literatureSame topicCanadian Identity and HistoryFrench-language works237,207