Turning Songs into Poems and Poems into Songs: Intersections of Literary Sinitic and Vernacular Korean in Chosŏn Literature
Bibliographic record
Abstract
Abstract This article investigates the dynamic intersections of Literary Sinitic and vernacular Korean and their impact on the innovations in poetry and song in fifteenth- through nineteenth-century Chosŏn Korea. More specifically, it traces the evolution of poetry or song discourse and explores the different strategies employed by Chosŏn poets and songwriters to render oral songs into text. It also investigates the differing views on the function of poetry and song, musical and textual preservation, and emotional and lyrical immediacy, which influenced the composition and translation of song-poems. The article probes the creative collaboration and competition between Literary Sinitic and vernacular Korean, and the fluid relations between translation and vernacularization. On the whole, it explores the ways in which the evolution of poetry-song discourse and the ensuing literary innovations contributed to Chosŏn's complex linguistic ecology.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".