Opportunities of Development of Eco-Tourism in the Karelian Arctic in the Conditions of the Existing Environmental and Social Challenges
Bibliographic record
Abstract
The formation of a competitive tourist space in the Arctic regions is expedient from the standpoint of diversification of predominantly single-industry local economies and increasing the socio-economic sustainability of local communities. However, it is extremely important that the measures aimed at achieving these effects are correlated with the ecological and social context of the territories and, fully using their existing potential, do not lead to an aggravation of ecological and economic risks. The purpose of this work was to assess the prerequisites for the development of eco-tourism in the example of the Arctic region and economically related territories and consider the possibilities of forming ecotourism zones. Based on statistical data, cartographic materials, and content analysis of semi-formalized interviews of experts, this work investigated the current level of socio-economic development of the Karelian Arctic, the existing tourist infrastructure, natural, and cultural-historical objects. Strengths and constraints of eco-tourism development are emphasized. A number of innovative tools and approaches for the development of ecological tourism in the Karelian Arctic were proposed, the introduction of which will increase the tourist attractiveness of the territory, and ensure its sustainable development by reducing negative environmental impacts and depopulation.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".