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Record W3121713980 · doi:10.7202/1072980ar

Boudoir Scissorhands: Matisse, the Cut-outs and the Canon

2020· article· fr· W3121713980 on OpenAlexvenueno aff
William Wood

Bibliographic record

VenueRACAR Revue d art canadienne · 2020
Typearticle
Languagefr
FieldHealth Professions
TopicHistory, Culture, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Physiquement diminué par la maladie et ébranlé par la Guerre, Matisse entreprend une série d’oeuvres qui l’amènent thématiquement, formellement et techniquement hors des sentiers battus par sa propre pratique et par la tradition moderniste dont il constitue l’une des grandes balises. Il s’agit de travaux décoratifs effectués à partir de papiers découpés, composés avec l’aide d’assistants et destinés à des objets utilitaires : livres, écharpes, bannières, vitraux et murales en céramique. Celui que ses tracés déliés et sa virtuosité de coloriste ont consacré maître de la peinture, s’abandonne aux aléas de la reproduction mécanique et aux spécialistes des métiers d’art. La relative position de faiblesse qui semble l’orienter dans cette voie ne mène cependant pas Matisse à une révision radicale du canon de l’art moderne (les critiques de Matisse sont ici complices de son conservatisme). Alors que ses expériences d’art appliqué connaissent un certain succès et le rappellent à l’attention du public, il s’inquiète des risques encourus dans l’entreprise. Celui pour qui facture rime avec signature se réclame toujours de la même originalité créatrice. C’est pourquoi il finira par abandonner l’univers du multiple pour le grand papier découpé autographe, lieu d’une projection fantasmatique où l’artiste invalide retrouve toute sa puissance de contrôle et d’infinie délectation.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.103
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.010
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.001

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 head0.034
GPT teacher head0.277
Teacher spread0.243 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2020
Admission routes1
Has abstractyes

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