Bibliographic record
Abstract
ESP textbook plays an important role in facilitating students to develop their profession-related language skills. However, ESP textbooks published in China are less developed and often criticized as ignoring the training of language skills. This research aims to reveal the specific problems of China’s ESP textbooks by conducting a multiple-case study. Three ESP textbooks used by ESP courses participants from G University in China were selected: “Computer Professional English Course” “Advertising English” and “Logistics English”. The research investigated their performance focusing on six aspects: coverage of language skills, text features, coverage of discourse functions, recycling, organization and difficulty. The content was analyzed by three different corpus tools. It is found that the three textbooks place too much emphasis on reading and vocabulary, lacking the training of listening skill, speaking skill, as well as the delivery of certain learning strategies. All three textbooks involve a wide range of discourse functions. The texts are informative academic texts, but organized by subject matter only, rather than a synthesis of subject matter, language points and language skills. There is scarce recycling of language points in two of the books and texts through all of them do not indicate a rising difficulty. It is concluded that the drawbacks of the three ESP textbooks far outweigh their merits. By uncovering problems of three ESP textbooks in China the research provides useful reference for future ESP textbook compilation.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".