Teaching Translation for Electric Power English From the Perspective of Relevance Theory: Belt and Road Initiative and Electric Power Education
Bibliographic record
Abstract
With the continuous development of the Belt and Road international cooperation of electric power, companies related to electric power develop overseas markets and invest overseas electric power projects. The market demand for translation of electric power English is increasing. Translation for Electric Power English is a basic required course for English major students in North China Electric Power University. The course combines translation skills with basic electric power knowledge in the process of teaching to help students gain professional knowledge and improve students’ practical language application in international electric power cooperation activities. Most of English majors master little knowledge about electric power, which makes it difficult for them to finish translation task with high quality of translation. Translation for Electric Power English Course should be taught in the following aspects: help students accurately understand the meaning of the original materials, aid them in selecting appropriate translation methods, modify the translated text and improve their translation ability for electric power English. Translation for Electric Power English Course focuses on imparting the translation methods of electric power English to students, such as literal translation, free translation, amplification and omission. This paper discusses the importance of the teaching of the course and uses Relevance Theory to analyze the teaching methods of Translation for Electric Power English.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".