Kateri Lemmens, <i>Nihilisme et création. Lectures de Nietzsche, Musil, Kundera, Aquin</i>
Bibliographic record
Abstract
Le sujet semble éviter toute forme d'épuisement, et les adages -« on ne fait pas de littérature avec de bons sentiments » et autres « professeurs de désespoir » -reviennent comme un terreau fertile où faire pousser les évidences : la littérature, la bonne littérature, sera sombre, triste, vraie, crue.L'entreprise de Kateri Lemmens, avec Nihilisme et création, est précisément de dépasser la déconvenue taillée par ces adages pour remonter à la racine de ce désespoir intrinsèque dont est, dont doit être fait l'art : elle en revient à Nietzsche, dans un fascinant état de la question qui, en lui-même, suffit à justifier l'ouvrage.Des analyses des œuvres et des pensées de Robert Musil, de Milan Kundera et d'Hubert Aquin permettent ensuite de mettre les concepts en mouvement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".