Russian Size Adjectives Analyses by Corpus Linguistics Methods
Bibliographic record
Abstract
The paper analyzes the functioning of size adjectives, exact as well as average size adjectives in Russian. Accurate measurement requires a numeral and also an adjective corresponding to the unit of measure. A parametric adjective is non-obligatory, while the designated parameter is derived from the semantics of the defined noun. The parametric noun in the required case form is included in the noun group, which performs the function of the attribute. Average size adjectives with abstract parameter nouns form complex attributes, taking pre and post positions to the defined noun. The adjective cредний /average is mainly combined with height and length parameters with the dominance of the noun size. Size adjectives короткий / short and длинный / long have a 2.4 asymmetrical ratio index of their functioning. It should be noted that roads and streets are more often characterized by positive and negative parameters, in turn. Adjectives of a positive width parameter dominate in garment characterization. This survey's primary objective is a qualitative analysis of the structure and semantics of size adjectives of exact measurement.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".