La mémoire du texte : Une lecture du livre Ce que nous devons aux anciens poètes de la France de Michel Zink. Zink, Michel. 2018. Ce que nous devons aux anciens poètes de la France. Paris: Collège de France.
Bibliographic record
Abstract
Temps et mémoire, irréductible couple au fondement des études de lettres médiévales menées par Michel Zink, sont revisités dans ce livre à l’occasion de la leçon de clôture prononcé le 10 février 2016. L’analyse des récits fondateurs préexistant à chaque communauté et remettant en question la notion même de vérité historique, où chaque histoire implique la possibilité d’une autre, ou bien, la conception de la poésie comme un récit, celui du passé afin de raconter, de mettre à l’écrit, la mémoire du présent constituent autant de raisons qui motivent l’étude des littératures du Moyen Âge. C’est ce que l’auteur tentera à tout le moins de nous rappeler, sinon de nous montrer au terme de la lecture.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".