(Heritage) Russian Case Marking: Variation and Paths of Change
Bibliographic record
Abstract
Russian’s six cases and multiple noun classes make case marking potentially challenging ground for heritage speakers. Indeed, morphological levelling, “probably the best-described feature of language loss”, has been substantiated. One study from 2006 showed that Heritage Russian speakers in the USA produced canonical or prescribed markers for only 13% of preposition+nominal sequences. Conversely, another study from 2020 found that Heritage Russian speakers in Toronto produce a 94% canonical case marker rate in conversational speech. To explore the effects of methodological differences across several studies, the current paper circumscribes the context to preposition+nominal sequences in Heritage Russian speech from the same Toronto corpus as used by the 2020 study but mirroring the domain investigated by Polinsky and including a Homeland comparison to consider changes in both the rates of use of canonical case marking and distributional patterns of non-canonical use. Regression models show more canonical case marking in more frequent words, an independent effect of slightly more mismatch by later generations, but less morphological levelling than reported by Polinsky. Lexicon size does not predict case marking rates as strongly as language usage patterns do, but generation, since immigration, is the best-fitting social predictor. We confirm (small) rate changes in Heritage (vs. Homeland) Russian canonical case marking but not in patterns of levelling.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".