Bibliographic record
Abstract
The essay aims at the study of the legal terms in legal English. From the analysis of the definition, characteristics and classification of the legal terms, the essay illustrates by a lot examples translation skills of different classifications of the legal terms. The author of this essay hopes that the essay can do much help to the learners, the researchers and the legal English dictionary compilers. Key words: legal English, legal term, translation skill Resume Cette these a pour but l’etude des mots terminologiques dans l’anglais juridique. A partir de l’analyse de la definition, la differenciation et les caracteristiques, elle propose par nombreux d’exemples les moyens de traduction de differentes classifications telles que la terminologie generale, particuliere, specialisee et empruntee. L’auteur voudait fournir des moyens d’apprentissage pour des apprenants et des chercheurs dans l’apprentissage de l’anglais juridique. Mots-cles: l’anglais juridique, terminologie juridique, moyens de traduction 摘 要 本文通過對法律英語中的專門術語的概念、分類和特點進行分析，提出了對於法律英語專門術語中通用術語、特別術語、專門術語和借用術語的漢譯技巧。通過實例分析，旨在為法律英語的學習者和研究者在學 習法律英語的過程中提供一些可資借鑒的學習技巧。 關鍵詞：法律英語；專門術語；翻譯技巧
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 | 0.007 | 0.032 |
| 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 | 0.017 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".