Bibliographic record
Abstract
Japan has always exhibited a serious attitude towards the translation of foreign languages. Any discussion of this issue must take into account the fact that ancient Japan borrowed Chinese characters, called kanji, from China. In this paper, I will focus on the use of kanji in translation, which has rarely been discussed in Western Translation Studies. Since ancient times, the Japanese have read imported books written in kanji according to a method called kundoku, which is still used for reading Western languages in modern Japan. Kanji was also used for writing the Japanese language. This article deals primarily with examples of kanji used to translate Western words. Cultures that use kanji, including Japan, have long trusted its expressive ability, which is why Japanese translators used this so‑called ideogram. In modern times, Japanese translators have used kanji to express the meanings of Western words. Of course, this type of usage has its limits when trying to express meanings from other cultures. On the other hand, this method of translation is fairly efficient: although people reading a kanji may not at first understand its full meaning, they perhaps feel that it has a serious meaning that can be roughly understood from its context. I call the assumption of meaning triggered by kanji the “cassette effect.”
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".