Bibliographic record
Abstract
Japanese publications on translation are markedly more numerous than Western publications. They are aimed at the general public rather than at professionals or academics, and few are truly scientific or academic. They deal with the Japanese context, with hardly any reference to foreign publications, authors, ideas or translation activities. They are also short-lived and disappear from bookstores and publishers' stocks within a few years. Theoretical translation texts are "philosophical" rather than scientific. Didactic texts are often aimed at language learners rather than at would-be translators. Linguistic translation texts are more interesting for the insight they give into the Japanese language and its use than for their contribution to translation theory. Texts that criticize published translations are numerous and very popular, something which is rather unique in the world. Many translation books are highly personal and contain numerous anecdotes from their authors' lives. Interpretation books are interesting, as they are more pragmatic than Western texts on the same subject, and address questions that Western publications seldom or never refer to. Machine translation articles are becoming increasingly popular. They tend to be confined to superficial explanations of the operation of systems and to descriptions of commercial products. Truly scientific papers on MT also exist, but their circulation is limited to academic and technical circles. There are a few periodicals dealing with translation. Most of the articles they carry are written by the same authors and have the same characteristics as the texts described above. On the whole, they are more interesting than translation books, as they are shorter and therefore denser. Articles on translation can also be found in countless books and periodicals on the Japanese language, on linguistics, sociology, public speaking, etc., as well as in weekly and monthly magazines and in other publications. This paper is followed by a list of Japanese texts on translation and by a list of Western language texts on translation of Japanese or on subjects relevant to the understanding of Japanese translation problems.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.018 |
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".