Les constructions partitives pronominales en français : une analyse de corpus
Bibliographic record
Abstract
Contrairement aux autres langues romanes, le français exige la présence de la préposition entre dans les constructions partitives pronominales: plusieurs d’entre nous; *plusieurs de nous. Le présent article poursuit deux objectifs : 1-documenter les contextes qui favorisent ou exigent la présence de la préposition entre dans ces constructions pronominales et 2- fournir une analyse quantitative de la variation. Pour ce faire, nous avons procédé à l’analyse comparative de deux corpus de journaux : le journal Le Monde 2002 et un corpus de journaux canadiens de la même époque. Notre analyse quantitative permet de distinguer les déterminants qui sélectionnent d’entre de façon catégorique ou quasi-catégorique des déterminants qui permettent la variation entre de et d’entre et de montrer l’existence d’un effet lexical significatif dans le choix de la variante, et ce, dans les deux corpus.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".