A la recherche du temps perdu de Marcel Proust : soixante minutes chrono
Bibliographic record
Abstract
Dans sa performance Tentative de résumer la Recherche du Temps perdu en une heure, l’artiste Véronique Aubouy propose un concept qui tient à la fois du conte, de la littérature et du théâtre. Elle réalise aussi un défi contre la montre qui ressemble à un challenge sportif. Réalisatrice, elle a passé l’essentiel de sa vie d’artiste derrière une caméra, rendant hommage à Proust dans un film-fleuve intitulé Proust lu. Le passage du film à la scène trouve sa cohérence dans la volonté de rendre hommage à un artiste dont l’œuvre a pris une place centrale dans sa vie. En pleine lumière, elle continue à se mettre en retrait par rapport au livre qu’elle veut mettre en valeur. Ce faisant, elle crée une forme artistique très originale, inédite sur la scène contemporaine. Mots-clés : performance, oralité, conte, littérature, théâtre, réception, Proust
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".