English for Tourism and Hospitality Purposes (ETP)
Bibliographic record
Abstract
The quick development of the tourism and hospitality industry can straightly influence the English language which is the most widely used and spoken language in international tourism in the twenty-first century. English for tourism has a major role in the delivery of quality service. Employees who work in the tourism and hospitality industry are entirely and highly aware of its importance and they need to have a good command of English in their workplace. English for tourism and hospitality has been categorized under English for the specific purpose (ESP). It is an important and dynamic area of specialization within the field of English language teaching and learning. The necessity of teaching English for professional purposes and specifically in the area of tourism is irrefutable. Language proficiency is very important and essential in all professional fields specifically in the tourism and hospitality industry due to its specific nature and concepts. Thus, it is required that the educators understand the practical applications of this approach. This paper aims to provide an overview of the purpose of teaching ESP (English for Specific Purposes) and ETP (English for Tourism Purposes) to the learners and users. In addition, characteristic features of ESP and ETP concerning course development, curriculum planning, learning style, material development, English efficiency, types of activities and evaluation are outlined. Determining the ESP concepts and elements provides specific English instruction that could help the learners be well-prepared for meeting their workplace requirements.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.016 |
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".