Exploring Business English Talent Training Model under the Background of the Belt and Road Initiative
Bibliographic record
Abstract
After thirty years of rapid development, China has become the second largest economy in the world. In order to strengthen economic cooperation with countries along the Belt and Road and assume more responsibilities as a major power, China has put forward the Belt and Road Initiative, which puts great pressure on the demand for Business English talents in society. However, at present, there is a shortage of high-level Business English talents in China. The traditional Business English talent training model cannot meet the needs of the business industry, which results in the serious disconnection between the cultivation of business talents and the market demand. Under the background of the Belt and Road Initiative, how to cultivate Business English talents has become a strategic issue. This research firstly combs the research of Business English talent training objectives and training model from the perspective of ESP Needs Analysis Theory, and then discusses the drawbacks of the current Business English training model from such aspects as curriculum setting, teaching system, and evaluation system, and gives suggestions in order to explore an appropriate Business English training model that meets the needs of the Belt and Road Initiative.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| 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".