L’enseignement de la traduction japonais-français : une formation à l’analyse
Bibliographic record
Abstract
Most Western translators of Japanese do not have quite a perfect understanding of the language. Some linguistic features of the Japanese language and its use by the Japanese also make it more difficult to translate than most other languages : its elliptic nature, its less than explicit logic, its grammar which provides few indications as to relations between nouns and noun clauses and few indications regarding time, its rapidly changing vocabulary and the rather loose way in which the Japanese tend to pose problems in translation . A third major problem for translators working from Japanese in the West is the lack of Japanese documentation and the difficulties encountered whenever they try to find Japanese resource persons to help them out with difficulties. Consequently, analysis is a must in translation from Japanese. Lexical analysis is mainly morphological in the case ofKango and phonological in the case of Gairaigo. Logical analysis of texts is necessary in testing meaning hypotheses, as the apparent "linguistic" meaning of text segments may be quite different from their true meaning. For complex, long or seemingly "agrammatical" or "illogical" sentences, the so-called "block analysis", which consists in identifying "blocks" encompassing noun phrases, identifying relationships between them, then streamlining sentences structurally and semantically until problems are pinpointed or solved, is an efficient analysis tool.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".