Teaching Specialized Vocabulary by Integrating a Corpus-Based Approach: Implications for ESP Course Design at the University Level
Bibliographic record
Abstract
The purpose of this study is to demonstrate how to integrate two in-house specialized corpora into a university-level English for Specific Purposes (ESP) course for nonnative speakers of English. The ESP course was an introductory level of wine tasting for Applied English Department students at a university specializing in hospitality in Taiwan. Two corpora of wine tasting notes selected from the official website of the Liquor Control Board of Ontario (LCBO) in Canada, one for red wine and one for white wine, were compiled. Lexical density and vocabulary compositions were analyzed. The results show that the lexical density and the percentages of specialized vocabulary of the wine corpora were higher than in other disciplines. In addition, wine reviewers used different vocabulary to describe the characteristics of white wine and red wine. From the keyword analysis, terms related to cooking methods and food names appeared in high frequencies. Based on the corpora analysis results, vocabulary lists, the LCBO website, and the in-house corpora were introduced to the students as supplementary materials. The pre- and posttest results for vocabulary indicate that the students enrolled in this program gained significant progress in both content and language knowledge. Based on the study results, recommendations for ESP teaching and materials development are discussed.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".