Observations sur l’enrichissement lexical dans la progression vers un japonais « langue passive » pour l’interprétation de conférence
Bibliographic record
Abstract
Few Westerners have sufficient proficiency in Japanese for conference interpretation. The major stumbling block in their acquisition of Japanese as a passive language resides in vocabulary enhancement.Japanese vocabulary consists o/wago, kango and gairaigo and their compounds. Each category has different characteristics in terms of learning. While learning gairaigo is rather easy for the Westerner, wago proves more difficult to memorize, and kango poses special problems due to the small number of distinct syllables in Japanese as opposed to the large number of kanji used. The large number of words used in Japanese compounds the difficulty, especially as compared with the acquisition of a Western language where the large proportion of words having common Greco-Latin roots that can be recognized even at first sight, reduces the number of new words that actually have to be learned. These facts provide one explanation for the difficulty Westerners have in reaching an adequate level of comprehension of Japanese for interpretation purposes. They also raise questions as to the soundness of the philosophy interpreters' schools and their methods in developing high-level linguistic skills. Up to now, this question has been dealt with on the basis of" common sense " and the instructors' personal experience. Data obtained through scientific research may significantly contribute to an improvement of the situation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".