Identity Construction of Chinese Business English Teachers from the Perspective of ESP Theory
Bibliographic record
Abstract
As an interdisciplinary major, Business English has a distinct difference from General English. Therefore, Business English teachers and General English teachers are also very different. However, nowadays, most Business English teachers in most colleges are with the educational background of English Language and Literature. They are facing serious problems of teacher identity construction. As Business English is generally considered to be a branch under English for Specific Purpose (ESP), there is a close connection between Business English and ESP. Therefore, ESP theory can provide a theoretical basis for the development of Business English program and can also provide some guidance for the identity construction of Business English teachers. Based on Needs Analysis and learning-centred approach of ESP theory, combining former researches of this field and observation findings of actual Business English classroom in a university, this article summarizes three identities that Business English teachers should construct: teaching practitioners and researchers, learners, and businesspeople. The study of teacher identity is of great significance to the successful construction of multidimensional teacher identities for Business English teachers and to the realization of their professional development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".