Semantic agreement in Russian: Gender, declension, and morphological ineffability
Bibliographic record
Abstract
Abstract In this paper, I argue that declension classes are not primitives (see Aronoff 1994; Alexiadou 2004; Kramer 2015; i.a.), but are decomposed into simpler features, one of which is gender (Harris 1991; Wiese 2004; Caha 2019). The argument is based on semantic gender agreement in Russian, where a grammatically masculine noun can trigger feminine agreement if its referent is female (Mučnik 1971; Pesetsky 2013). Semantic agreement is grammatical only in those forms where a regular nominal exponent is syncretic with an exponent of a declension class that includes feminine nouns. In other forms, conflicting masculine and feminine gender features lead to ineffability in morphology (cf. Schütze 2003; Asarina 2011; Coon and Keine 2020). Ineffability arises because the Subset Principle (Halle 1997) that holds between features of a vocabulary item and a terminal at the point of Vocabulary Insertion is violated later in the derivation. This is in turn possible if Vocabulary Insertion applying cyclically bottom-up (Bobaljik 2000) is interleaved with Lowering that alters structure below a triggering node (Embick and Noyer 2001). Finally, I show that Russian also has a number of cases where conflicting gender features in a noun phrase do not result in a realization failure (Iomdin 1980). The difference between these patterns is derived in a principled way and follows from the positions where conflicting features are introduced.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".