Bibliographic record
Abstract
Nous proposons une présentation des fonctions lexicales de l'approche Sens-Texte qui met à jour et précise les présentations antérieures, en tenant compte, notamment, des apports des travaux théoriques et descriptifs en Lexicologie Explicative et Combinatoire. Après une introduction qui explique l'importance de l'étude et de la maîtrise du système des fonctions lexicales, nous caractérisons la notion même de fonction lexicale, en nous focalisant sur les fonctions lexicales dites « standards », leur classification et leurs propriétés (section 1). Nous présentons ensuite les fonctions lexicales standards des deux classes majeures : fonctions paradigmatiques (section 2), puis fonctions syntagmatiques (section 3). Nous concluons sur l'implication des fonctions lexicales dans la structuration des modèles lexicaux de type réseaux lexicaux, plus particulièrement ceux appelés « Systèmes Lexicaux » (section 4).
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.020 |
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".