Les noms propres et leurs dérivés dans le vocabulaire de l’intelligence artificielle
Bibliographic record
Abstract
Les noms propres et leurs dérivés dans le vocabulaire de l'intelligence artificielle – Cet article présente les résultats d'une étude qui porte sur un sujet relativement peu exploré par les terminologues, à savoir la part des noms propres et de leurs dérivés dans la formation des vocabulaires techniques et scientifiques. Nous nous intéressons en particulier dans cet article aux onomastismes qui font partie du vocabulaire de l'intelligence artificielle. L'article se divise en trois parties. Nous présentons tout d'abord les matrices morphosyntaxiques qui composent les onomastismes de l'intelligence artificielle. Nous étudions ensuite les unités terminologiques complexes onomastiques du point de vue du nom propre et de son réfèrent. Enfin, nous examinons ces mêmes unités du point de vue de leur déterminé.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".