Bibliographic record
Abstract
With the development of computer and the Internet, traditional teaching methods have been greatly challenged, so does ESP teaching. In order to cope with the new trend and the changes happened in classroom teaching and learning, teachers should turn their eyes on more effective methods especially the ones integrated with computer and the Internet technology. English for Specific purposes (ESP) has a long tradition, as a movement designed to respond to non-English majors’ needs both for academic and cross cultural communication purpose in specific scientific fields and professional settings. The purpose of ESP teaching may lay both in explaining basic language knowledge used in a particular subject and promoting the abilities to use English as a tool or a way to learn the special subject. How to teach ESP and are there any new methods can be applied into classroom teaching are remain the hot topic which have been discussed recently. Nowadays, in this information age, many scholars devote themselves to the exploration of computer aided teaching in particular subjects, and computer aided translation is of great importance in ESP teaching. It is a subject involves the basic concepts of computer-aided translation technology, helps students learn to use a variety of computer-aided translation tools, enhances their ability to engage in various kinds of language service in such a technical environment. This study explores the method of applying basic principles of computer aided translation in ESP teaching, including using searching engine or an appropriate electronic dictionary to translate a term, using proper software to construct a terminology database, using corpus to refine an academic writing and etc.. It can not only combine modern technology with traditional teaching but can also enhance the ESP learners’ ability in reading and translating, and to make them increasingly autonomous .
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".