Marketing Policy with Targeting to Attract New Customers to Ecological Recreation Areas in the Context of Sustainable Development of the Region
Bibliographic record
Abstract
The purpose of the study is to analyze and improve modern aspects of marketing policy with targeting to attract new customers to environmental recreation areas in the context of sustainable development of the region.The object of the study is a separate tourist area and the state of its sustainable development.The scientific question is how to improve the marketing policy system of an individual tourism area in the context of sustainable development.To solve this issue, the expert research method, the iterative consensus method, as well as the Saaty method and dual comparison matrix method were used.As a result of the study, key tourist areas in Norway were analyzed and those most suitable for the development of marketing policies were identified, targeting the attraction of new customers in the context of the sustainable development of the region.At the same time, the long-term growth of tourists was calculated through the use of new elements of marketing policy with targeting to attract new clients to ecological recreation areas in the context of sustainable development of the region.The study has a limitation, since it takes into account the peculiarities of the functioning of tourist areas exclusively in Norway.Future studies are expected to expand the study to other countries.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".