Bibliographic record
Abstract
At the present stage of Ukraine’s development, its tourism resources are extremely limited compared to the leading mass and cultural-educational tourist destinations of the world, which creates the need to find alternative directions for the development of the Ukrainian tourism market. Rural tourism has been successfully practiced abroad for many years, which in the future can become a full-fledged component of the tourism market of Ukraine and a guarantee of its tourist attractiveness. In order to successfully implement these potential innovations in the Ukrainian tourism sector and prevent mistakes already made by the pioneers of rural tourism, the experience of developing this type of tourism abroad requires detailed study, which became the aim of this article. Analysis, generalization and classification were used as research methods. The publication defines approaches to the regulation of rural tourism; factors of diversification of approaches to stimulation and regulation of rural tourism development are identified; functions of the State regulation related to the development of rural tourism are classified; the experience of rural tourism development of Cyprus, Sri Lanka, Canada, India, the USA, Australia, Great Britain and Germany is analyzed, general and distinctive features are distinguished; the instruments used in the framework of organizational and economic mechanisms to support rural tourism abroad are defined; approaches to supporting the development of rural tourism are formulated according to the criterion of intensity of efforts on the part of the State and regional management bodies; the role of rural tourism in the development of agriculture is substantiated; the key factors of using foreign experience in the rural tourism development in Ukraine are identified. According to the results of the carried out research, rural tourism has every chance to be successfully implemented in the tourism market of Ukraine.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".