Bibliographic record
Abstract
This book is a unique collection of research papers representing current directions in Russian language studies in Canada and the United States. The book is integrated thematically by its focus on Russian language structure and dynamics, as well as by the regional themes pertinent to the maintenance and acquisition of the Russian language in the US and Canada. Traditionally, Slavic and Russian studies in these countries have involved mostly literature, history, politics and culture. This collection of research papers reflects recent changes in Russian studies with a focus on language structure, language use, pedagogy and teaching methodology. At least four major trends are responsible for these changes. First, the rapid economic and social changes in Russia that occurred after the collapse of the Soviet Union in combination with the development of information technology have trigged an unprecedented change in the language structure and use, which now attracts the attention of linguists (e.g., Ryazanova-Clarke and Wade 1999). The lexical system of modern Russian is characterized “by an increased instability of the boundaries between the centre and the periphery” (Ryazanova-Clarke and Wade 1999, 75), i.e., some words from the periphery are moving into the center, while some central words are marginalized. Words change their meanings and undergo re-connotation; the morphological word formation system is extremely active; new loan words appear in abundance; and the grammatical system registers changes in preposition use, acquires a larger class of indeclinables and displays a growing tendency towards analyticity (Ryazanova-Clarke and Wade 1999).
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.382 | 0.242 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".