A Brief Introduction on ESP Teaching Current Situation and the Countermeasures in Higher Vocational Colleges
Bibliographic record
Abstract
International competition is becoming more and more impetuosity. Global integration process is to speed up. English is as a bridge language connecting the world and naturally becomes the main; the most widely used international language. Since English is a language, so in English teaching we should focus on cultivating students’ ability of applying English. ESP (English for Specific Purposes) follows this principle. It refers to a specific profession or related disciplines, setting up English courses according to the learner’s particular purpose or the specific need. Its purpose is to cultivate students use English to communicate with others in a certain work environment, such as Business English, Legal English, Tourism English, Automobile, Computer English, English of science and technology, Engineering English , etc.. Until now, few people can directly use English for their scientific research or serve their work, life and study. Therefore, ESP courses are imperative in the higher vocational colleges. In addition, it is also the requirements of social progress and education reform, the demands of the market, the needs of integrating with the world education in the future. In order to make the ESP teaching be better implement the higher vocational English teaching, the author objectively analyzes and proposes the solution measures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".