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Record W3006219565 · doi:10.7202/1066733ar

Derision, Nonsense, and Carnival in the Work of Greg Curnoe

2020· article· fr· W3006219565 on OpenAlexfundvenueaboutno aff
Katie Cholette

Bibliographic record

VenueRACAR Revue d art canadienne · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Dans les années 1960, l’artiste canadien Greg Curnoe était le protagoniste du très dynamique milieu artistique de London en Ontario. Il y créa une série d’ateliers indépendants, y exposa ses oeuvres dans différents lieux et y conçut plusieurs performances audacieuses. Curnoe était une personnalité extravertie qui adhérait de façon inconditionnelle aux principes du mouvement Dada : rejet de normes esthétiques, adhésion à « l’anti-art », utilisation du hasard, de l’aléatoire et de l’absurde. Cependant, ses oeuvres empreintes d’humour, tout comme ses activités à parfum anarchique, ne relevaient pas simplement d’un esprit moqueur. En effet, tout au long de sa carrière, Curnoe eut recours à des stratégies humoristiques inspirées de Dada et à certains aspects du carnavalesque visant à contester le milieu des arts, mais aussi à brouiller les frontières entre l’art et la vie. Par là, il entendait créer une communauté d’esprit dans laquelle il pourrait librement faire évoluer sa pratique. Ce texte se donne pour objectif de mettre en évidence le rôle central qu’ont joué les stratégies inspirées du mouvement Dada et le carnavalesque dans la vie et l’oeuvre de cet artiste.

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)0.004
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: none
Teacher disagreement score0.819
Threshold uncertainty score0.360

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0320.054
Scholarly communication0.0070.003
Open science0.0010.008
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.198
Teacher spread0.178 · 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

Citations3
Published2020
Admission routes3
Has abstractyes

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