Russian Language History in Canada. Doukhobor Internal and External Migrations: Effects on Language Development and Structure
Bibliographic record
Abstract
Introduction At present, there are about 30,000 Doukhobors in Canada (a higher estimate, i.e., 40,000, is given in Tarasoff 2002, ix): 12,300 in British Columbia; 8,000 in Saskatchewan; 3,000 in Alberta and the rest in other provinces (Popoff 1983, 117). Language maintenance among the Doukhobor population is estimated at about 60 percent, although this figure contains a large number of semi-speakers, especially among the younger generation (see Schaarschmidt 1998). The present linguistic analysis will concentrate on the effects the internal and external migrations of the Doukhobor community have had on the structure and development of the language beginning with the settlement in Milky Waters in 1802. We shall present four synchronic slices in the development of Doukhobor Russian: (1) the formation stage of a compromise language in Milky Waters (Section III); (2) the leveling process in the Transcaucasian stage (Section IV); (3) the development of three functional styles in the early years in Canada (Sections V and VI); and (4) the slow but inevitable erosion of these functional styles especially since the 1940s (Section VI). Even before the Doukhobors' mass emigration to Canada in 1899, their language was distinct from both Standard Russian and Russian dialects, first as a result of the resettlement from all parts of the Russian Empire to the Crimea, and later, the forced resettlement of the group from the Crimea to Transcaucasia, i.e., to an area with non-Slavic populations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".