Bibliographic record
Abstract
This study aims at suggesting concrete action plans to establish a feasible and effective public-sector translation system in South Korea based on an extensive review of overseas advanced models such as those of Australia, Canada, China, the Netherlands, Sweden and European Union. Specifically, some possible ways to set up a government body responsible for the public-sector translation as well as to introduce a national system to manage translator pool are discussed. The overseas models offer three implications for South Korea to pay attention to.As a first step to prepare for establishing a government public-sector translation body, focused and clear policy objectives need to be defined based on a thorough and precise survey on the demand in various fields for public-sector translation. In South Korea, public-sector translation is in need to help foreign workers and married immigrants settle in the society as well as to promote international economic cooperation, inbound tourism, and national image. After defining the policy objectives, systematic and specific ways to organize and manage the body in accordance with its essential functions should be investigated. Among many functions, a South Korean public-sector translation body should be equipped with an objective and accurate evaluation and revision system as well as various translation infrastructure, which include national translation standards, terminology database, and translation memory. As an effective national system to manage translator pool, certification or registration systems led by the government or professional association are being most commonly adopted. Overseas models also suggest that such a system should grant different levels of certification or registration to translators based on their qualification, and review the qualification of certified or registered translators on a regular basis to ensure the service quality and active practice of those who are working or will work in the public sector.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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; both teacher heads agree on what is shown here.
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".