Nutrition et lexicographie : le champ lexical ‘nourriture’ (huit vocables dans le Dictionnaire explicatif et combinatoire du français contemporain)
Bibliographic record
Abstract
Ce travail, qui s'inscrit dans le cadre du Dictionnaire explicatif et combinatoire du français contemporain, propose les articles de dictionnaire de huit vocables faisant partie du champ lexical ‘nourriture’ : NOURRIR, SE NOURRIR, NOURRISSANT, et cætera. À l'encontre de ce que laissent entendre les dictionnaires courants, on démontre que le champ ‘nourriture’ est sémantiquement sous-jacent au champ ‘aliment’. À partir des définitions dans les dictionnaires courants, on retrace les sens les plus fondamentaux à l'intérieur du champ ‘nourriture’, on établit des ponts sémantiques entre les lexèmes d'un même vocable et on propose un ordre selon lequel les lexèmes des vocables en question devraient être définis. On examine ensuite les conséquences de notre desription sémantique de SE NOURRIR sur les définitions des verbes pronominaux dans le DEC, et on justifie certaines composantes sémantiques de nos définitions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".