English for General Academic Purposes or English for Specific Purposes? Language Learning Needs of Medical Students at a Chinese University
Bibliographic record
Abstract
The debate over the appropriacy of EGAP and ESP has been an ongoing concern in many higher education contexts. In this paper we discuss how teachers’ and students’ perceptions of English curriculum needs are reflected in the conflict between short-term goals, such as passing exams, and long-term goals, such as career development. Students, doctors and teachers at a medical university in the central part of China were asked about their needs through questionnaires and structured interviews. The findings suggest that whilst many felt the need for medical English to be taught in the early years, particularly through medical texts, there was also push back due to the need for general English to pass English exams, such as CET4/6. We argue that through the incorporation of medical texts, students can start to develop their medical English from the first year of university. This not only ensures the motivation for students to study medical English for professional purposes, but also fulfills the perceived need to prepare for the exam.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".