Bibliographic record
Abstract
The history of the reception of the Russian Symbolist movement in English begins in the 1890s. Readers in Great Britain and the United States could read about the Russian Symbolist Fedor Sologub long before any of his works were translated into English. During World War I and a parallel wave of interest in Russia, Sologub is one of the most popular Russian writers in the English-speaking world. Some of his poetry and prose works are translated into English and during the years 1915-1950 are included in no fewer than 28 Englishlanguage anthologies. During the first years of this period, almost all of his prose that is accessible to English readers is selected and translated by two translators, John Cournos and Stephen Graham. His poetry, on the other hand, is selected and translated by several translators over the course of this entire period. Anthologies with works by Sologub appear in two main waves: from 1915 until the middle of the 1920s, and in the 1940s after the outbreak of WWII. These anthologies demonstrate how Sologub was presented to English-speaking audiences during these years. This article examines English-language anthologies from this period, comparing what, if anything, is said about Sologub in their introductions to the works by Sologub they include. Some presented him as the quintessential decadent, while others tried to show the various sides of Sologub’s works. It is often the case in anthologies that the opinions of Sologub presented by editors are not supported by the works by Sologub these same editors selected for inclusion. The article ends with three bibliographical appendices listing Sologub’s anthologized poetry and prose and the anthologies that included them.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".