Le domaine vitivinicole en France et en Espagne : similitudes et variabilité conceptuelles
Bibliographic record
Abstract
Les domaines vitivinicoles français et espagnol sont très proches. Les réseaux conceptuels correspondants peuvent sembler à première vue identiques. Cependant, il y a des différences. L’objet du présent travail est d’étudier la variabilité conceptuelle entre les deux domaines afin d’en analyser les implications terminologiques et traductionnelles. Cette réflexion est nécessaire pour éviter notamment des équivalences erronées. Nous traiterons de différents plans de variabilité : des concepts, des objets et des communautés de travail. Après avoir établi les concepts principaux structurant le domaine vitivinicole, leur organisation conceptuelle est étudiée, puis les entités et les activités spécifiques des deux domaines sont analysées de manière contrastive.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".