Pour des Malherbe : actes [de la journée d'étude, Musée des Beaux-Arts de Caen, 24 et 25 novembre 2005]
Bibliographic record
Abstract
Publiés sous la direction de Laure Himy-Piéri et Chantal Liaroutzos. Ont participé à ce colloque:Franck Bauer (Professeur, Université de Caen Basse-Normandie), JeanFrançois Castille (PRAG, Université de Caen Basse-Normandie), JeanDelabroy (Professeur, Université ParisVII), Gilles Henry, Laure HimyPiéri (Maître de conférence, Université de Caen Basse-Normandie),Marie-Gabrielle Lallemand (Maître de conférence, Université deCaen Basse-Normandie), Chantal Liaroutzos (Maître de conférence,Université de Caen Basse-Normandie), Gisèle Mathieu-Castellani(Professeur émérite, Université ParisVII), Bruno Méniel (Maître deconférence, Université de Rennes 2), Emmanuelle Mortgat-Longuet(Maître de conférence, Université de ParisX, Nanterre), Bruno PeteyGirard (Maître de conférence, Université de ParisXII Val-de-Marne),Guillaume Peureux (Université de Toronto), Gérard Poulouin (PRAG,Université de Caen Basse-Normandie) et Marie-Noëlle Vivier-Gallardo(Bibliothèque municipale, Caen).
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.010 |
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".