Inscriptional Repertoires and the Problem of Intra- versus Interlingual Translation in Traditional Korea
Bibliographic record
Abstract
Abstract This article brings together a series of examples demonstrating the wide range of inscriptional practices in premodern Korea and the ways in which they force us to reconsider modern and Eurocentric notions of translation. The premodern inscriptional spectrum in Chosŏn Korea was not a simple binary of cosmopolitan orthodox Literary Sinitic versus vernacular Korean in the form of ŏnhae exegeses but was a range of inscriptional styles that included idu and kugyŏl. The ways in which texts were inscribed, reinscribed, and transliterated between these different inscriptional styles, as well as the ways in which Chosŏn literati themselves understood the notion of yŏk (譯, “translation”) challenge modern-day notions of translation, on the one hand, but also invite an understanding of them as rather more intralingual than interlingual. They also force us to ask whether LS was conceived as a “foreign” language for literate Koreans in Chosŏn. The premodern Korean cases forces us to add script and inscriptional repertoire (including notions of orthography, notational system, munch'e 文體, etc.) to the list of the main factors that influence intralingual translation.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".