The System of Grammatical Categories of the Verb in Kazakh, Russian, and English
Bibliographic record
Abstract
The relevance of the study is conditioned by the fact that the verb is a part of speech that expresses the grammatical meaning of an action, so there is a sign of a dynamic flow in time. The verb is the only part of speech that has analytical forms. The specificity of a verb is a dependent grammatical meaning that binds verbs concerning an action. They do not contain the semantics of the restriction in the action that they denote, their boundary can be considered as defined from the outside, but not as a result of the verb semantics. The purpose is to consider and compare the system of grammatical categories of the verb in Kazakh, Russian, and English. The following methods were used: linguistic, comparative, and structural. The basic unit of grammar is the grammatical category. It combines grammatical forms with a single grammatical meaning. Whole, homogeneous, and opposite grammatical forms of a particular language are called a paradigm. When analysing categories, it is especially important to consider the unity of semantic and formal plans: if there is no plan, then this phenomenon cannot be classified. Grammatical categories of each language can be a kind of questionnaire for describing objects and situations in that language.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".