Bibliographic record
Abstract
Cet article présente une analyse des SD focalisés préposés en russe. Contrairement à l’analyse antérieure de King 1995, nous affirmons qu’il existe deux possibilités syntaxiques en russe pour focaliser un syntagme à partir d’une phrase déclarative neutre: une dans la périphérie gauche et l’autre dans la périphérie droite, entre le ST et le Sv. Nous démontrons que ces deux positions sont les positions de spécifieurs de la projection fonctionnelle SFoc. En nous basant sur deux tests d’exhaustivité, celui de Szabolcsi 1981 et celui de Farkas 1998, nous proposons que ces deux positions se distinguent à l’égard de l’exhaustivité, ce qui expliquerait la hiérarchie qu’on retrouve dans le positionnement des SD focalisés en russe. Les deux positions sont susceptibles d’accueillir les syntagmes contrastés, mais seule la position du [Spec, SFoc] de la périphérie gauche peut accueillir les syntagmes exhaustifs.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".