An Integrated Approach to Business English Teaching in China UNE APPROCHE INTEGREE A L'ENSEIGNEMENT DE L'ANGLAIS DES AFFAIRES EN CHINE
Bibliographic record
Abstract
Chinese Business English Teaching (BET) has developed more rapidly since China’s entering the World Trade Organization (WTO). However, traditional BET has its problems such as too much emphasis on business knowledge and professional vocabularies, which leads to learners’ lower practical language ability. There is a great necessity to solve the problems to meet the increasing demand for high quality business talents being able to work competently in the context of economic globalization. Based on sound language teaching theories and practices, this paper proposed an integrated approach to BET which aims to cultivate business expertise rather than just teach professional terms and knowledge. Key words : Business English Teaching (BET); Integrated approach; Task-based teaching; Application of video; Text structure analysis Resume L’enseignement de l’anglais des affaire en Chine (BET) s’est developpe au plus rapidement depuis la Chine est entre dans l’Organisation mondiale du commerce (OMC). Toutefois, BET traditionnelle a ses problemes qui sont trop l’accent sur la connaissance des affaires et des vocabulaires professionnels, ce qui conduit a la capacite des apprenants de langue inferieure pratique. Il ya une grande necessite de resoudre les problemes afin de repondre a la demande croissante pour les hauts talents d’affaires de qualite etant capable de travailler avec competence dans le contexte de la mondialisation economique. Base sur de solides theories d’enseignement des langues et des pratiques, ce document propose une approche integree de BET qui vise a cultiver l’expertise d’affaires plutot que de simplement enseigner des conditions professionnelles et de connaissances. Mots cles : L’enseignement de l’anglais des affaires (BET); L’approche integree; basee sur les tâches d’enseignement; L’application de la video; Analyse de la structure du texte
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".