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Record W4406679639 · doi:10.14712/23366680.2024.2.7

Identity Games and Polemics Between the Arts: Marcel Proust and Claude Jutra

2025· article· en· W4406679639 on OpenAlexaboutno aff
Eva Voldřichová Beránková

Bibliographic record

VenueSlovo a smysl · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)The artsArtPhilosophyPsychoanalysisArt historyAestheticsPsychologyVisual arts

Abstract

fetched live from OpenAlex

Take It All (1964) is a remarkable autofiction film in which the Quebec filmmaker Claude Jutra (1930–1986) responds to Proustian theories about the superiority of literature (a ‘pure art’) over cinema and other arts based on ‘direct imitation of reality’. The article first summarizes Proust’s sceptical attitude towards cinema, and then analyzes the way in which Jutra attempts to rehabilitate this art through autofictional procedures. Using Deleuze’s concept of the ‘time-image’ (l’image-temps), Ju tra proves that cinema, like the Proustian novel, is capable of practicing polyphony, multiplying nar rative identities, and finding surprising connections between details of events from different time zones. Despite their diverging views, it is possible to note numerous points of contact between the two authors. Both Proust and Jutra agree on a practice of autofiction (avant la lettre, of course) that turns the life of an individual into a kind of interpretive key to the universe and a means of opening the eyes of the reader/viewer.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.112
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.032
Scholarly communication0.0110.007
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.313
Teacher spread0.296 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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