Bibliographic record
Abstract
This paper focuses on introducing three different English translations of “Yellow Birds Song” which was published in the early 20th Century. It compares three different translations, explains the differences of the background stories fromthe original content, and discusses the translators’ ideologies in translation and the purpose of Korean poem translations. The three translators are James S. Gale, a Canadian missionary, Joan S. Grigsby, a Scot poet, and Younghill Kang, Korean-American novelist. They translated the poemof “Yellow Birds Song” which was written in 17 BC by King Yuri of Koguryo, and they cited the translated poeminto the book ofHistory of the Korean People(1928, a book of history),The Orchid Door(1935, a book of poetry), andThe Grass Roof(1931, novel) respectively. They each used the same poembut written in different languages. These different original poems influenced their translations. Gale used the poemwritten in Chinese, Grigsby used the English translation by Gale, and Kang used the Korean song. Gale’s translation is similar to Chinese poem. Grigsby’s translation is free style because she couldn’t understand the original content and tried to adjust to the western style. Kang’s translation is close to the original Korean song. These three translations are different because of their different ideologies in translation. First, Gale put an importance in Korean literature and understanding ofWestern readers. Thus, he tried to be faithful to the original and at the same time, compose his translation as an English poem. Second, Grigsby didn’t even try to translate close to original content, because she felt that characteristics of Korean literature was not important in translation. She thought literal translation of Korean poems couldn’t appeal to average western readers. Third, Kang did his best to be faithful to the original Korean song because he thought Korean songs had ardent sense. These translators translated the background story of the book of SamKookSaKi(三國史記) differently because they have different purpose of Korean poem translations. First, Gale tried to provide the story in detail, and added his opinions as a missionary and historian because he wanted to introduce Korea as a country with a long history and rich literature. Second, Grigsy summarized the story very succinctly because she focused on the ancient beauty of Korean poems rather than the long history, Third, Kang changed whole story in his novel because he tried to show the harsh reality of those days when it was under the Japanese control. His translation of Korean song expresses their pride and deep grief over the country lost.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".