Bibliographic record
Abstract
In Korea, the translation boom started from the late 1990s. Compared to other countries, including China, Japan, Canada, etc., the translation boom is quite late, so the translation patterns have not been transparent. However, for the last 20 years or so, the translation patterns began to be noticed by others, including the association of translators. It’s not a news that the education of translation in Korea was not active 20 years ago, as there was not any organization to teach students through the public education system to be translators. When people thought of translation, they thought that anyone can do it if they wished to do so; in other words, translation was not important, was not taken seriously and was not a major subject. After 20 years, thanks to the investments into the translation education, Korea’s translation got more images to define itself: more professional and sophisticated. With the introduction of Artificial Intelligence (AI), we need to make translations more advanced. As we need the translations to be more professional, it is important to know, who will be translators in the future. With this in mind, I created surveys for undergraduate students who take lectures in general English, a required subject in collegiate education in Korea, with the students from varying majors. The surveys’ questionnaire include: their translation interest in the future, their thoughts about the general English and the translation conditions in the present day, the relation between translation and their major, who they think should do the translations, the degree to which translation is needed in collegiate education, if they want to be translators or learn translation as a major, etc. With the analysis with the results of this survey, I can picture myself a snippet of the future that holds in translations. I hope that translations will come to be brighter and more transparent..
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.037 |
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".