Bibliographic record
Abstract
Peut-on formuler des conditions non empiriques pour qu'un objet ou un fait soit reconnu comme ayant fonction de symbole ? Si oui, il conviendrait de les nommer conditions proto-logiques, car elles concernent des formes, comme la logique, mais sont plus primitives que les déterminations logiques mêmes. L'objet de cet article est de discuter cinq notions qui peuvent être présentées comme candidates à cette fonction d'universels proto- logiques pour les langues naturelles : la pluralité des niveaux d'« articu- lation», l'énoncé complet, la notion d'« ancrage» de l'énoncé à renonciation, le nom propre, la corrélation rhème-thème. De tels universaux ne sont pas présentés comme des classes de symboles, mais des types de fonctions diversement réalisées dans des structures grammaticales. Les mettre en évidence - ou en découvrir d'autres - devrait être l'une des tâches de la philosophie du langage dans sa relation à la science linguistique.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".