Bibliographic record
Abstract
L’idée d’épuration de la langue a une longue histoire en français, depuis le xvii e siècle ; elle eut un effet majeur, pas toujours positif, sur notre idiome. Un courant de pensée, minoritaire, mais brillamment illustré, a toutefois plaidé pour la richesse lexicale, la bienveillance envers la néologie, la tolérance envers la variation et la diversité des normes. La francophonie, expansion mondiale du français, qu’accompagne une impressionnante vitalité innovante, donne raison à cette famille d’esprit. La mondialité de l’idiome s’accompagne d’une profusion qui fait sa richesse. Afin d’en rendre compte, la rédaction d’un dictionnaire répond à une tradition, lexicographique et politique. Le projet d’un dictionnaire des francophones, commande publique, initiative française, mais réalisée en concertation francophone, outil numérique cumulatif, participatif et évolutif, devrait tourner la page de l’épuration lexicographique en donnant à voir le dynamisme de la langue, en invitant à y puiser.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".