Research on the Education System of Business English Courses Based on the Case of GDUFS
Bibliographic record
Abstract
<p>In order to develop a more scientific education system of Business English courses, this paper studies the case of Guangdong University of Foreign Studies (GDUFS, one of Chinese top-three foreign language universities) by questionnaires. The result shows that: 1) Most of the students hope that the school could add more business courses in the curriculum setting, as it can help them to find better jobs or it will be more consistent with their interests and expectations when they decide to choose this major. 2) Students hope that they have more opportunities to practice their English listening, speaking and business practice skills. 3) There are many difficulties when students are learning business English, so the school should add some related courses to help students to deal with these problems. Based on the empirical analysis and the findings of this research, some suggestions are proposed for the improvement of the education system.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".