Bibliographic record
Abstract
Comme Roland Barthes, Claude Simon aurait pu écrire « Proust, c’est ce qui me vient », tant Marcel Proust est omniprésent dans l’ensemble de son oeuvre, se cache dans la plupart de ses recoins, les plus lumineux comme les plus sombres. Qu’il le prenne comme modèle de ses propres expérimentations dans ses entretiens, puise dans son oeuvre des leçons de composition, partage avec lui le goût des métaphores, déstructure et parodie ses analyses psychologiques, en fasse un personnage de ses romans, le regarde travailler et relire ses épreuves ou décrive avec lyrisme ses phrases « d’une mortelle somptuosité », il semble le lire et le relire sans fin. En suivant quelques fils de lecture, qui parfois s’entremêlent — la mémoire, les haies d’aubépines, les rats, la peinture ou les poissons cathédrales —, cet article tente de lire Simon lisant Proust, de lire Proust écrit par Simon, de lire Simon en prenant par Proust, de (re)lire Proust à travers Simon, etc.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".