State and problems of public management in the field of green tourism: regional aspect
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The article considers the phenomenon of green (eco-) tourism within the concept of sustainable tourism, as well as the vectors and overall landscape of public management in this field. Best practices, challenges and concerns existing in the plane of green tourism in Ukraine, Canada, and Australia are analyzed in details. It is revealed that the main problem of green tourism of Ukraine is the lack of systematicity and insufficient attention from public administration bodies, while the locations, specifically in Western Ukraine, have excellent nature and human potential, and high motivation of green tourism development is observed. A whole-of-systems approach that comprehends not just the tourism value chain but also larger economic and socio-cultural systems in which tourism is embedded is proposed as being necessary for a green recovery and transition. It is also essential to consider the symbiotic relationship that exists between tourism and ecological restoration and protection. Developing long-term strategies that outline a vision for a sustainable future for tourism, including the desired contribution to economic, environmental, and social wellbeing, implementing a combination of evidence-based policies and interventions to promote greener tourism practices, developing and improving monitoring frameworks to measure progress through the provision of robust and meaningful data and indicators, encouraging the development of sustainable tourism experiences that generate positive outcomes for the environment and visitors, and innovating in experience design to give visitors the opportunity to learn about and participate in green tourism are some of the key policy considerations outlined based on the analysis discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it