INTERNATIONAL MODELS OF TOURISM BUSINESS DEVELOPMENT IN SMALL COMMUNITIES
Bibliographic record
Abstract
In the current conditions of searching for effective tools for the sustainable development of small territorial communities, tourism is of particular importance as a catalyst for economic activity, employment, and preservation of cultural heritage. At the international level, a number of models of tourism business development in small communities have been formed that can be adapted to the Ukrainian context. However, their implementation requires in-depth analysis, critical reflection, and consideration of local characteristics. The purpose of the study is to summarize international experience in tourism business development in small communities and determine the possibilities of its adaptation to Ukrainian conditions; the object is tourism development models, and the subject is the mechanisms of their functioning and the conditions for implementation at the local level. The study uses a comparative analytical method to compare tourism development models in the EU, Japan, and Canada; a case method to analyze successful examples (in particular, the projects “Aldeias do Xisto” in Portugal and “Satoyama Tourism” in Japan); expert interviews with specialists in tourism cluster development; as well as a SWOT analysis to assess the strengths and weaknesses of Ukrainian small communities in the field of tourism business. As part of the study, the author typified international models of tourism development in small communities, identified key factors of their success, and formulated recommendations on the possibilities of their adaptation taking into account Ukrainian socio-economic and institutional conditions. According to the results of the study, it was established that the key conditions for the successful development of tourism business in small communities are intersectoral cooperation, support for local initiatives, investments in infrastructure, an effective marketing strategy, and involvement of the population as an active subject of change. The implementation of pilot projects based on communities with high tourism potential, the formation of cluster associations, the use of digital promotion platforms, and the involvement of EU institutions in co-financing tourism initiatives are recommended.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".