Relation entre le sens des noms et leur structure prédicative
Bibliographic record
Abstract
Dans cet article, on soutient que l’on peut étudier le sens des noms prédicatifs en s’appuyant sur la théorie des verbes supports. Selon cette théorie, les noms sont des prédicats qui ont pour forme de base une phrase, par exemple respect a pour forme de base la phrase à verbe support avoir = :N 0 a du respect pour N 1 . À chaque emploi d’un même nom, on peut associer une forme spécifique de ce type. On a étudié ici comment classer, par exemple, les différents sens du mot grammaire tels qu’ils apparaissent dans les phrases Jean a fait une grammaire du français, Le français a une grammaire complexe, Jean fait de la grammaire , et comment les relier entre eux, en les confrontant aux noms « composés » étude grammaticale et structure grammaticale .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".