The Forming English Lexical Competence in Dialogic Speaking for Prospective Experts of Hospitality and Restaurant Service
Bibliographic record
Abstract
Hospitality and restaurant service are two of the fastest growing industries in the world.Knowing how to speak English is the most important skill to have for hospitality and restaurant service jobs.In the getting job process we faced the problem that the students to not have enough skill in dialogic speaking.In the article the author describes the forming English lexical competence in dialogic speaking for prospective experts of hospitality and restaurant service.To use a dialogic speaking is difficult as a dialogue needs alternate use of students' abilities the speaker and then to express their own thoughts and ideas.Lexical competence surely includes the size of vocabulary and the thematic range.English lexical competence is designed to help students train the following: hotel management, reception, concierges, housekeeping, restaurant staff, tour guides, and most other hotel staff positions.There is a global need for prospective experts of hospitality and restaurant service who can speak English and interact with international guests.Analyzed the topics of the course, jobrelated areas and situations necessary in forming lexical competence.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".