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Record W4394731985 · doi:10.62212/1866/32876

Industrialisation du dessin animé aux États-Unis

2023· book· fr· W4394731985 on OpenAlexfundno aff
Jean-Baptiste Massuet

Bibliographic record

Venuenot available
Typebook
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsArtHumanitiesPolitical science

Abstract

fetched live from OpenAlex

Lorsque l’on évoque le terme « dessin animé », un certain imaginaire se déploie assez instantanément, imaginaire qui repose en grande partie sur un modèle industriel hérité des productions de Walt Disney. La logique technique qui sous-tend ce modèle a une longue histoire, qui s’inaugure en grande partie aux États-Unis dans un contexte qu’il convient d’interroger. Les années 1910 témoignent en effet d’une série de tâtonnements et d’expérimentations permettant de concevoir des dessins animés dans un temps réduit afin d’en accroître la production, et donc la rentabilité. En ce sens, la mise en place par l’industrie du dessin animé de méthodes calquées sur l’imaginaire technique de la caméra de cinéma s’avère moins le fait d’un désir d’intégration aux formes filmiques dominantes que d’une tentative de mécanisation de la production d’images en mouvement.

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)0.001
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 score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.002

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 head0.476
GPT teacher head0.340
Teacher spread0.136 · 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
Published2023
Admission routes1
Has abstractyes

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