Derivational Grammar Model and Basket Verb: A Novel Approach to the Inflectional Phrase in the Generative Grammar and Cognitive Processing
Bibliographic record
Abstract
Generative grammar was a true revolution in the linguistics. However, to describe language behavior in its semantic essence and universal aspects, generative grammar needs to have a much richer semantic basis. In this paper, we took a novel morpho-syntactic approach to the inflectional phrase to account for the very diverse inflectional phrase qualities in different languages. Some languages show a very different surface verbal inflection, providing evidence of a different mental processing at the semantic level. In fact, the inflectional phrase is a great representative of the mental and semantic processing layers in mind. Therefore, in this study, we analyzed the inflectional phrase with a novel approach to take into account this rich verbal inflectional configuration in languages, and to describe why some languages behave in a different way in the spatial and temporal aspect. In this study, we analyzed and discussed the verbal inflectional structure of several languages, including German, Swahili, Persian, English, and Indonesian, and our result is the introduction of a semantic model which provides a much richer insight to the semantics/syntax interplay.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".