The eastern path of exile: Russian women's writing in China
Bibliographic record
Abstract
No one will hear your voice in the depth of the night Whether you cry out or not … MARIIA VIZI , Noktiurn 2 (Nocturne 2, 1970s) Histories of Russian literature have all too often ignored the Far Eastern emigration. Part of the reason for this neglect lies in the very nature of the emigration itself. Unlike the émigré communities in Paris, Berlin, and New York where in time the Russian community gradually became integrated with the native population, the Russian émigrés in China never assimilated. Essentially they remained outsiders to the country and culture which they inhabited, living an entirely Russian life with rare instances of understanding or assimilating Chinese culture. Apart from specialists and interpreters, ordinary Russians did not learn Chinese the way émigrés learned French, Czech, or Serbo-Croatian or the other languages of Europe. Estranged from Chinese life, the émigré population was similarly cut off from émigré life in Europe. Distance and political upheavals prevented many of their literary journals and newspapers from finding their way to the West. Thus it was that the Russians living in China from 1917 through the post World War II years found themselves torn not only from the country they had left but from the European émigré communities as well, a fact which may account for why so little critical attention has until recently been paid to this chapter in Russian literary history.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".