Bibliographic record
Abstract
Cet article fait état des différences syntaxiques qui distinguent les usages attributif et possessif du verbe avoir , usages illustrés par des phrases du type elle a les yeux verts et elle a des yeux verts , respectivement. Il est proposé que ces différences découlent des propriétés lexicales des deux verbes : avoir attributif est un verbe sémantiquement vide qui régit une proposition prédicative réduite, alors que avoir possessif, sémantiquement plein, sélectionne un argument externe et un argument interne. Ainsi, les différences syntaxiques entre les deux constructions (type de NP postverbal (aliénable ou inaliénable), nature du déterminant (défini, non défini), possibilité de modification par un adjectif qualifiant, type de prédicat secondaire (état passager ou propriété permanente) sont attribuables au rôle que doit jouer dans la structure le NP post-verbal, soit prédicatif avec avoir attributif et argumental avec avoir possessif.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".