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Record W2123817360 · doi:10.7202/1006726ar

Fidget de Kenneth Goldsmith, entre poésie visuelle et norme procédurale

2011· article· fr· W2123817360 on OpenAlexvenueno aff
Yan Rucar

Bibliographic record

VenueProtée · 2011
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophyArt

Abstract

fetched live from OpenAlex

Issu d’une procédure contraignante, le texteFidgetde Kenneth Goldsmith apparaît sur plusieurs supports. Ces versions respectent toutes l’intégralité du texte d’origine, mais en affectent radicalement l’aspect visuel. Sur l’écran électronique,Fidgetfait apparaître un langage qui, sous l’effet de jeux de texture tels que l’encombrement et la mobilité, succombe à un graphisme aliénateur. Le langage deFidget, fragmentaire dès l’origine du texte, se prête en vertu de cette structure à une insertion dans une composition qui n’est pas proprement textuelle ou picturale, mais participe de ces deux domaines. Or, des textes picturaux, pétris tout à la fois de significations figurales et de désignations linguistiques, émanèrent de la pratique poétique concrétiste, instaurée par le groupeNoigandresde São Paulo à partir de 1957. Poète visuel, Charles Bernstein propose dansVeil(1976) l’exemple opposé d’une textualité écrasée par la figuralité. Comment la version électronique deFidget, avec ses facteurs visuels, interagit-elle avec le modèle procédural ? Comment le texte devient-il une composante d’un écran combinant mobilité et visualité ? Cette interrogation sera informée par le modèle antérieur de la pratique concrétiste de 1957 et par l’oeuvreVeilde Charles Bernstein.

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)0.009
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: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.018
Scholarly communication0.0060.008
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0080.003

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 head0.332
GPT teacher head0.312
Teacher spread0.020 · 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
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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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