Russian reduplicative surface-syntactic relations in the perspective of general syntax
Bibliographic record
Abstract
The paper considers lexical reduplications in Russian in the perspective of general syntax. The goal is to define and fully characterize special Russian surface-syntactic relations [SSyntRels], that is, the reduplicative SSyntRels, which appear exclusively in syntactic idioms formed by lexical reduplications. The syntactic operation REDUPL is defined, and several reduplicative SSyntRels are introduced. A deductive calculus thereof is proposed, based on three parameters concerning the correlations between the reduplicate and the reduplicand: the reduplicate is anteposed/postposed (with respect to the reduplicand); is in contact/is not in contact (with the reduplicand); represents an exact/inexact copy (of the reduplicand); eight reduplicative SSynt-Rels are theoretically possible. The notion of syntactic idiom (a non-compositional multilexemic expression having a non-segmental signifier) is formulated and illustrated: e.g., the sentence Mne Y prazdnik X ne v prazdnik Lʹ(X) lit. ‘To me the feast is not into a feast’ = ‘I cannot enjoy the feast’, which implements the syntactic idiom [X to.Y] ˹be not into Lʹ(X)˺ ‘X cannot be enjoyed by Y’. Six reduplicative SSyntRels of Russian and one of English are described. These SSyntRels are conceived as a fragment of a general inventory of SSyntRels in the world languages.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".