The Concept of Sustainable Rural Tourism Development in the Face of COVID-19 Crisis: Evidence from Russia
Bibliographic record
Abstract
In the context of globalized processes, the importance of the sustainable development concept in solving the problems of local tourism systems development is growing. Unprecedented challenges caused by the COVID-19 crisis in the tourism sector, on the one hand, questioned the possibility of fulfilling the Sustainable Development Goals (SDGs) and the goals of sustainable tourism. On the other hand, they emphasized the need for balance between three pillars of sustainability, both as an urgency tool to cope with the pandemic crisis and as a solid basis for long-term development in the post-pandemic period. The study presented in the paper discusses sustainability issues in rural tourism as one of the most promising sectors for the development of domestic tourism on the example of the Russian tourism industry. The overall goal of the study initiated in the pre-pandemic period is to find ways to support sustainable rural tourism in Russian regions and to develop indicators for monitoring the effectiveness of local strategic development programs, taking into account national and regional specifics. This paper discusses intermediate results obtained with the adjustment for pandemic challenges. The authors combined a number of methods and techniques, namely desk research, statistical analysis, and analysis of empirical data obtained by means of in-depth interviews, as well as a survey using a formal questionnaire. The results confirm that Russian enterprises and local communities considered the three pillars of sustainability as important to develop tourism in rural destinations both in the pre-pandemic period and in times of challenges caused by the COVID-19 pandemic. At the same time, the findings show weaknesses in the federal and local policy, including the lack of systemic measures to improve the sustainable management of Russian tourism destinations. From the authors’ point of view, it makes sense to adapt the European tourism indicator system for sustainable destinations (ETIS) for local peculiarities. ETIS is a useful tool to boost the sustainable development of rural destinations by encouraging stakeholder engagement and monitoring processes. In the case of Russia, one needs to add indicators for monitoring the effectiveness of the implementation of strategic development programs in the field of tourism.
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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 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".