MULTICULTURAL COMPONENT IN THE SYSTEM OF PROFESSIONAL TRAINING OF TOURISM AND HOSPITALITY MANAGERS IN CANADIAN UNIVERSITIES: EXPERIENCE FOR UKRAINE
Bibliographic record
Abstract
The article deals with tourism and hospitality managers’ professional training in Canada. The authors analyse the scientific and pedagogical literature which highlights different aspects of the problem of multicultural component in the system of professional training of tourism and hospitality managers. The research methodology comprises theoretical and applied methods (bibliographical method, analysis and systematisation, content analysis, statistical analysis, synthesis and comparative analysis, mathematical method). The development of tourism and hospitality managers’ professional training is outlined and the emergence of the need for its multicultural component formation is specified. The analysis of tourism occupational standards made it possible to distinguish the knowledge, skills and professional values of tourism and hospitality managers. The formation of the multicultural component, the content, objectives, and priorities of multicultural training are based on three main principles of multicultural education: accessibility, equal opportunities, diversity. The article describes the theoretical and applied elements of multicultural component formation as well as basic forms of organisation: the development of special courses, including additional content elements in curricula and academic programmes, the use of innovative forms and methods of training. The proposals for possible ways to adapt Canadian tourism and hospitality managers’ professional training experience in Ukraine are developed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".