(Heritage) Russian Case Marking: Variation and Paths of Change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it