La modélisation psychomécanique des systèmes temporels : le cas du russe
Bibliographic record
Abstract
La démarche de la psychomécanique est en principe hypothético-déductive, la théorie prenant son départ à une exigence absolue — un « inévitable » — et procédant déductivement jusqu’à la rencontre avec les faits : Guillaume pose par exemple dans Temps et verbe (1965) que toutes les langues construisent leur systématique verbale à partir d’un présent universel α/ω). Le modèle qu’il élabore pour le russe comme celui de Meney (1974a et b, 1975), bien qu’incompatibles entre eux, acceptent ce postulat qui entraîne des problèmes de cohérence interne et, en bout de course, la non-satisfaction de l’exigence minimale d’adéquation d’observation. L’hypothèse de remplacement que je propose, limitée à l’indicatif, révoque sa division en époques au bénéfice de la combinatoire de deux systèmes aspectuels, l’un lexical, l’autre grammatical. Cette solution, qui range le russe parmi les langues exclusivement aspectuelles, s’impose par sa simplicité, sa cohérence, son accord avec la sémiologie et son efficacité sur le plan empirique.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".