MétaCan
Menu
Back to cohort
Record W1877343413 · doi:10.7202/1030230ar

Pantomime, danse et stylisation (Cary Grant, James Cagney, Fred Astaire)

2015· article· fr· W1877343413 on OpenAlexvenueno aff
Christian Viviani

Bibliographic record

VenueCinémas Revue d études cinématographiques · 2015
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

Music-hall et pantomime placent les acteurs qui en sont issus au carrefour de plusieurs formes artistiques et entraînent chez eux une stylisation antinaturaliste du jeu corporel. Trois cas, relevant du cinéma classique hollywoodien, sont analysés dans cet article : Cary Grant, James Cagney et Fred Astaire. De ces trois acteurs formés à l’école de la pantomime et de la danse populaires, un seul, Astaire, a privilégié le genre musical ; les incursions dans le film non dansé sont pour lui exceptionnelles, comme celles de Cagney, grande figure du cinéma criminel, dans le film musical. Quant à Grant, qui a su imposer un jeu non naturaliste, la formation chorégraphique et pantomimique est peut-être pour lui un moyen de mieux entrer dans le mystère de l’équilibre entre réalité et fiction des corps cinématographiques qu’il incarne. Les traces d’une formation chorégraphique ou pantomimique favorisent le passage du banal à l’exceptionnel sans entamer la crédibilité. Le filigrane féminin vient ainsi enrichir la virilité affichée. L’acteur de cinéma doit-il être un peu mime et danseur pour pouvoir proposer un corps proprement cinématographique ?

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.066
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0140.002

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 head0.075
GPT teacher head0.263
Teacher spread0.188 · 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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueCinémas Revue d études cinématographiquesSame topicCinema and Media StudiesFrench-language works237,207