Bibliographic record
Abstract
Abstract This paper examines the linguistic features of Korean-language publications issued in the Russian Far East (RFE) between 1922 and 1937, the year all Koreans in the RFE were deported to Uzbekistan and Kazakhstan, and tries to answer the questions “Can we speak of a separate ‘Soviet’ Korean written language, and if so, what were its defining characteristics?” Moreover, “If there was a ‘Soviet’ Korean written language, or at least the appearances of such, was this by design or by accident?” In order to answer these questions, the paper examines published materials in Korean from the RFE alongside metalinguistic statements about the Korean language and Korean language policy penned by relevant Korean intellectuals and Soviet commentators. The main argument is that we can indeed detect an incipient case of ‘language making’ and the beginnings of a distinct ‘Soviet Korean’ written language congealing in the years leading up to the deportation of 1937. But this was more by accident than by design, and owed on the one hand to the peculiar constellation of language policies, Soviet Korean language and orthographic ideologies, and Korean dialect facts in the RFE, and on the other hand to the relative shallowness of Korean language standardization on the peninsula itself. Any further developments in the way of Soviet Korean ‘language making’ were nipped in the bud by the deportation of 1937 and the discontinuation of Korean language education in schools from 1938. As a result, written Soviet Korean ceased to exist, and spoken Soviet Korean – Koryŏmal – became completed “unroofed”; the Soviet Koreans became a “rag doll nation” within the USSR, and spoken Soviet Korean/Koryŏmal became a “rag doll language.”
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".