Dérivées variables : mathématiques et littérature au XXIe siècle. Vers un lyrisme stochastique
Bibliographic record
Abstract
Ce travail s’ancre dans le retard, déploré par Kenneth Goldsmith, qu’ont pris les pratiques littéraires sur les pratiques artistiques dans la période contemporaine. L’application du terme de stochastique, issu des mathématiques, associé aux notions de probabilités, de combinatoire, permet de penser les dispositifs mis en oeuvre par certains écrivains pour déployer les possibilités enfermées dans le langage. Ces applications, plus ou moins rigoureuses, d’un dispositif mathématique conduisent à penser des pratiques d’écriture autant créatrices que libératrices. Culminant dans l’écriture d’une machine elle-même dirigée par des algorithmes, elles ouvrent à la possibilité d’un lyrisme stochastique qui, en suspendant les fonctions du langage, en permet la contemplation, au-delà de toute actualisation. Autant de dispositifs lyriques rendant à la poésie sa capacité de libération des dispositifs qui nous contraignent.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".