Bibliographic record
Abstract
L'etude de ce qu'il est convenu d'appeler la litterature de l'epoque medievale en langue romane pose, des Ie depart, bon nombre de difficultes. Ou commencer quand on met en cause Ie terme litterature meme pour decrire Ie contenu du corps de manuscrits preserve des cinq siecles anterieurs a Ia Renaissance? nest generalement admis (et surtout depuis les recherches de P. Zumthor aux annees 1980) que cette premiere difficulte de terminologie releve de l'importance de l'oral dans la culture medievale.1 Toute etude motivee par Ie desir de mieux comprendre l'oral et l'ecrit au moyen age bute, tot ou tard, contre des obstacles de methode provenant surtout de Ia grande distance temporelle qui existe entre nous et l'epoque medievale. Difficulte methodologique: on peut lire et analyser les manuscrits, mais aucune trace directement observable ne reste de Ia performance orale. Les conclusions que 1'0n peut tirer sur la compos ante orale de notre problematique doivent alors se baser sur une analyse des caracteristiques qui y sont indirectement liees. 2
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".