Bibliographic record
Abstract
Introduction : processus de normalisation et durabilité de l'information Vincent Liquète, Monica Mallowan et Christian Marcon 1 Hors norme ?Une approche normative des données de la recherche Joachim Schöpfel 4 Limites de la norme, constructions de la connaissance et durabilité de l'information Anne Lehmans 18 Normalisation et durabilité de l'information prescriptive en milieu scolaire REP+ Carole Véjux 33 Les représentations des risques numériques en éducation : construction de normes dans les discours en circulation Camille Capelle 46 Normalisation d'un outil lié à l'évolution des pratiques dans le cadre d'un nouveau champ de recherche patrimonial -le cas de la mission PATSTEC Valérie Joyaux 56 Faire avec les normes dans l'espace de la documentation scolaire Valentine Mazurier 69 Notes de recherche Le modèle SOLARIS pour pérenniser l'élaboration, le partage et l'hybridation de savoir dans la complexité Thomas Bonnecarrere 81 Entre processus de normalisation et durabilité de l'information digitale, vers une nouvelle dépendance à la faveur de l'énergie numérique Viviane Du Castel 98 Définir une norme pour l'éternité ?L'exemple des messages d'avertissement autour des sites de stockage de déchets nucléaires David Rochefort
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 | 0.017 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".