MétaCan
Menu
Back to cohort
Record W4313346879 · doi:10.4000/feeries.4528

Charles Perrault, cinématographe. Reprise, continuation et démontage de La Belle au bois dormant au cinéma

2022· article· fr· W4313346879 on OpenAlexaff
Alex Bellemare

Bibliographic record

VenueFéeries · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicHistorical and Literary Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

Les contes de fées de l’âge classique constituent un matériau privilégié de l’imaginaire merveilleux occidental, délivrant à la fois figures, scénarios et motifs. Les adaptations cinématographiques des contes de fées, parce qu’ils sont plastiques et modulables, sont nombreuses et variées, mais elles s’inscrivent toutes dans une dynamique sérielle qui définit différents rapports de réécriture et d’interprétation. Cette étude prend pour objet les effets de transformation et de variance qui découlent de l’adaptation cinématographique, et porte plus spécifiquement sur deux reprises filmiques de La Belle au bois dormant de Charles Perrault : La Belle endormie (2011) de Catherine Breillat et Belle dormant (2017) d’Adolpho Arrietta. Chez Breillat, la dominante est accordée à l’exploration onirique de l’enfance, en investissant de symboles l’un des points aveugles du conte de Perrault : le sommeil de la Belle. Chez Arrietta, la modernisation de La Belle au bois dormant repose sur la prolifération des médias employés comme supports narratifs (musique, objets magiques, photographie), permettant ainsi d’interroger les pouvoirs des récits et les fonctions de l’imaginaire. Enfin, les films de Breillat et d’Arrietta proposent, en marge des enjeux de reprise, une réflexion sur le dispositif cinématographique lui-même.

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: Other · Consensus signal: Other
Teacher disagreement score0.026
Threshold uncertainty score0.088

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 studies0.0030.004
Scholarly communication0.0050.005
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0260.006

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 head0.014
GPT teacher head0.277
Teacher spread0.263 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueFéeriesSame topicHistorical and Literary StudiesFrench-language works237,207