Bibliographic record
Abstract
Faisant écho à l’article « Interdisciplinarité et sérendipité » du professeur Philippe Walter, l’auteur se penche sur sa thèse de doctorat, rite de passage obligé des chercheurs universitaires. Il en évoque la genèse, la longue gestation, les appuis reçus, la soutenance puis l’édition finale avec la critique bienveillante qui l’avait fort satisfait. Trois décennies plus tard, un message inopiné l’informe que son livre a permis de « casser le code secret » du Conte du graal , « le plus grand des récits médiévaux français ». Et, geste éthique exceptionnel, le médiéviste qui l’a trouvé prépare deux ouvrages qui tirent ouvertement parti de l’essai de l’ethnologue. Ce dernier, étonné, échange alors autour de ce « miracle bibliographique ». En présentant les écrits récents du médiéviste, il refait ici le parcours d’une rencontre interdisciplinaire aussi heureuse qu’inattendue.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".