Проблеми та перспективи розвитку мисливського туризму в Україні
Bibliographic record
Abstract
The article is devoted to the analysis of problems and prospects of the hunting tourism development in Ukraine. Within the study, it was found out that hunting tourism as a specific type of tourism is developing rapidly in many countries around the world, including Hungary, Bulgaria, the Czech Republic, Austria, Romania, Belarus, Kyrgyzstan, Tajikistan, Tanzania, Botswana, Canada, Argentina, and others. This field is closely linked to the activities of many industries, and its development contributes to the creation of new jobs, differentiation of the national economy, rational use of natural resources and increase in the amount of financial revenues to the state and local budgets of these countries. Favorable natural conditions, diversity of wildlife, convenient geographical location and acceptable pricing policy create good conditions for the potential development of hunting tourism in Ukraine. Today, hunting tours to the Western regions of our country are in demand. A few years ago, tours to Kharkiv and Zaporizhia regions, as well as to the Crimea were quite popular among hunters. However, hostilities in eastern Ukraine, loss of territorial integrity of the state, as well as adverse social and economic situation significantly reduced the number of hunting tours to these regions. Today, domestic hunting tourism is a solitary tour, and therefore systematic hunting tourism is not discussed. We believe that successful development of this type of tourism is also hindered by many of the problems inherent in the hunting industry. These are the imperfection of the legal framework, low number of hunting animal species, lack of adequate infrastructure, services, related services, insufficient number of specialists, imperfect marketing policy and, as a consequence, loss of the hunting industry. Addressing these problems is a prerequisite for the successful development of hunting 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".