From Corpus to Classroom: Teaching Semi-technical Business English Vocabulary
Bibliographic record
Abstract
Semi-technical vocabulary has been considered a challenging and neglected area of English for Specific Purposes (ESP) instruction. This paper employs AntConc to extract a keyword list of a self-compiled business English textbook corpus (BETC). Through manual identification from corpus keywords, we focus on semi-technical vocabulary, addressing the fundamental question of “how to identify”. We also draw upon pedagogical materials from the academic sub-corpora within the Corpus of Contemporary American English (COCA) to design a corpus-based language pedagogy (CBLP) lesson. This lesson serves as a model for instructing the multifaceted meanings and diverse patterns of the semi-technical word “default” across various disciplines and contexts, addressing the question of “how to teach”. Our research leverages the rich resources provided by pedagogical corpora, offering in-depth analysis, including collocation, colligation, semantic preference, and semantic prosody, as effective teaching aids. In doing so, it promotes interdisciplinary, comparative, and exploratory teaching and learning of semi-technical business English vocabulary. By bridging the gap between corpus analysis and classroom instruction, it provides innovative strategies for educators in the field of business English, and by implication, in various ESP disciplines.
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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 | 0.001 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; a candidate call from one teacher head, 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".