Penser, classer, apprendre et communiquer. Normalisation et nouveaux modes de classification du savoir
Bibliographic record
Abstract
La mise en réseau numérique universelle de tous ne s’est pas faite sans un immense effort de développement documentaire, sémantique, traductique et normatif capable de répondre notamment aux bouleversements induits de la pluralité des cultures de création, de classification, d’apprentissage et de communication du savoir. Certes, la normalisation de l’e-learning (SC36) est emblématique de cette transformation mais c’est l’ensemble du système technique TIC qui est concerné et qui s’est mobilisé pour développer des socles de réponses (UNICODE, XML, Web sémantique). On peut penser que l’appropriation collaborative de ce nouveau système numérique aura des effets sur la transmission du savoir, notamment une autonomie pour apprendre en classant.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".