Bibliographic record
Abstract
The status of interpreters and translators depends on the society in which they belong. Such factors as whether the society is multilingual, monolingual as well as its international standing all impact their status and consequently financial compensation. A brief overview of the history of the status of Korean interpreters reveals that, in the past, they enjoyed middle class status and, at times, even great wealth. The social importance of translators, on the other hand, was negligible—a situation which was aggravated by the fact that readers were not very demanding. During the modern era, and especially with increased foreign trade in the 1980’s, however, such tolerance was no longer the norm. There is still great interest among the general public in interpretation, especially since speaking English fluently is considered an asset in any profession in Korea. Conference interpreters, as such, are considered to be “master” English speakers. While they are envied their fluent mastery of foreign languages, interpretation, as such, is not considered a profession in which one should devote one’s life. In the case of translation, though there are many translators, they are held in even lower esteem than interpreters because of the relatively low pay.
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 | 0.003 | 0.006 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".