Dire les sciences et décrire les sens : Entre vulgarisation et lexicographie, le cas des dictionnaires de sciences
Bibliographic record
Abstract
Dire les sciences et décrire les sens : Entre vulgarisation et lexicographie, le cas des dictionnaires de sciences – L'étude des stratégies rédactionnelles utilisées dans les dictionnaires spécialisés scientifiques permet de réfléchir aux façons de faciliter l'appropriation des connaissances. Ainsi, pour éviter les pièges tendus par des signifiants connus aux signifiés inédits, les relations lexicales sont précieuses, en particulier l'isonymie. Sur le plan syntagmatique, les relations predicatives sont utiles pour cerner la signification sur laquelle s'appuie la construction notionnelle. Les stratégies sont à contraster dans la mesure où les branches du savoir possèdent des traditions dénominatives diverses, dont une meilleure connaissance serait nécessaire tant pour une bonne description de ces vocabulaires que pour améliorer les propositions en matière d'aménagement terminologique.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".