Research Progress and Trend of Business English Writing Instruction in China (2002-2021): A Bibliometric Analysis
Bibliographic record
Abstract
Using the visual analysis instrument of China National Knowledge Infrastructure (CNKI) and CiteSpace, this study makes a bibliometric analysis and visual presentation of the literatures on Business English Writing Instruction in China during the last two decades (2002-2021). The examination of the number of published articles, authors and institutions, highly referenced papers, and keywords reveals that: (1) the number of published articles increases with the constant development of the business English major. It steadily reduced after reaching its peak but remained at a high level. (2) The distribution characteristics of authors and institutions are similar, which means that no single author or institution has an absolute edge in terms of the quantity of papers. (3) Although majority of the highly cited literatures are from CSSCI, the overall quality of the 433 articles chosen is not very high. (4) The research hotspots are mostly concerned with teaching content and methods, teaching mode and design, and Business English writing education in higher vocational colleges. (5) The research development process is generally divided into three stages: early investigation, prosperity and development, and sustainable development. Continuing research into Business English Writing Instruction will require that researchers conduct follow-up studies, increase quantitative analysis, and broaden the scope of research samples in order to better serve Business English majors in China as well as improve the quality of business English writing research in China.
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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 | 0.067 | 0.038 |
| 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; both teacher heads agree on what is shown here.
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".