La langue des signes, c’est comme ça
Bibliographic record
Abstract
Sous la direction de Mélanie Hamm Comité scientifique Maria Fernanda Arentsen, Université de Saint-Boniface, Canada Anne Bamberg, Université de Strasbourg, France Chérif Blein, Aix-Marseille Université, France Annelies Braffort, LIMSI, CNRS, Université Paris-Saclay, Orsay, France Sylvain Brétéché, Aix-Marseille Université, CNRS, PRISM, Aix-en-Provence, France Martine Faraco, Aix-Marseille Université, CNRS, LPL, Aix-en-Provence, France Jacques Goorma, Eurobabel, Strasbourg, France Médéric Gasquet-Cyrus, Aix-Marseille Université, CNRS, LPL, Aix-en-Provence, France Mélanie Hamm, Aix-Marseille Université, CNRS, LPL, Aix-en-Provence, France Michèle Kirch, Université de Strasbourg, Strasbourg, France Sibylle Kriegel, Aix-Marseille Université, CNRS, LPL, Aix-en-Provence, France Geneviève Le Corre, Collectif Des Sourds du Finistère, Brest, France Claire Maury-Rouan, Aix-Marseille Université, CNRS, LPL, Aix-en-Provence, France Agnès Millet, Université Grenoble-Alpes, Lidilem, Grenoble, France Nathalie Niederberger Costello, Lycée Français de San Francisco, États-Unis d’Amérique Béatrice Priego-Valverde, Aix-Marseille Université, CNRS, LPL, Aix-en-Provence, France Jean-Philippe Prost, Université Montpellier, CNRS, LIRMM, Montpellier, France Anne Tortel, Aix-Marseille Université, CNRS, LPL, Aix-en-Provence, France Nicolas Tournadre, Aix-Marseille Université, Institut Universitaire de France, CNRS, Lacito, Villejuif, France Daniel Véronique, Aix-Marseille Université, CNRS, LPL, Aix-en-Provence, France Alice Vittrant, Aix-Marseille Université, CNRS, Lacito, Villejuif, France
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.070 | 0.028 |
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".