L'apophtegme au goût de miel. Poésie, politique et philosophie dans le Recueil d'apophtegmes mis en vers français de Michel Mourgues
Bibliographic record
Abstract
Dans son recueil d’apophtegmes dédié au duc de Bourgogne, le jésuite Michel Mourgues consacre une bonne partie de l’ouvrage à des « sentences choisies par rapport à la profession des anciens Philosophes ». En faisant appel aux ressources de la poésie, Michel Mourgues entend contribuer à faciliter la mémorisation de ces apophtegmes, tout en concourant au resserrement extrême de l’expression. Mais en prenant des libertés par rapport à ses modèles, le poète cherche aussi à relever et à ennoblir sa matière. Il facilite l’accès du bon mot au rang de la poésie d’idées, où la poésie et la philosophie, et parfois même les sciences, sont dans un rapport d’imbrication, comme on le voit tout particulièrement dans le cas d’apophtegmes métamorphosés en fables philosophiques.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".