Tourism Industry Perspectives in the Context of the COVID-19 Pandemic Based on the Sustainable Development Concept
Bibliographic record
Abstract
The relevance of this study is in the growing popularity of the concept of sustainable development in the tourism sector. The purpose of the article is to determine a systematic basis for the assessment of the possibility of sustainable tourism development at the regional level, as well as determining relevant vectors. In addition, the study considers a number of studies that allow for defining sustainable tourism, as well as determining groups of indicators affecting it. The leading method of studying tourism structure as part of a regional socio-economic system is topological analysis, which allows for identifying functionally significant combinations of factors. Incidence matrices of the structure of indicators with included weighting factors influencing the sustainable development of tourism, analysis of their q-connectivity, the results of the dimension of simplexes, the number of connected components and communication chains, the structural vectors of the complexes were determined and presented. The study proved the presence of simplexes in the complexes. The effects on simplexes can bring the desired result in the quickest and most efficient way. Since tourism is an integral part of environmental, social and economic sectors, and the sustainable development itself can be regarded as a unified system of interaction between them, it is possible to use the above factors in each of the sectors on a case-by-case basis in any territory or enterprise to conserve resources, eradicate poverty and ensure well-being. This research attempts to formalize the factors that determine the sustainable development of tourist destination that gives the full basis for a systematic study of the territory to assess the sustainability of tourism development. The topological analysis shows the mutual influence of simplicial complexes by means of a chain of connections leading to sustainable development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".