The experience economy approach to marketing Les Plus Beaux Villages brand in Russia
Bibliographic record
Abstract
Purpose This study aims to analyze the possibilities for the development of an association of the most beautiful villages of Russia using an experience economy approach. Design/methodology/approach This study uses a case study approach based on the practices of the federation of the most beautiful villages of the Earth and the associations of the most beautiful villages of France, Italy, Japan, Canada and Germany. Findings Based on the analysis of the case studies of beautiful villages marketing in different countries and the methodology of the experience economy, the paper recommends essential changes in the management practices of the association of the most beautiful villages of Russia and its participants. Practical implications Several recommendations have been suggested for exploring, scripting and staging the experiences in beautiful villages of Russia. Originality/value The main output of this study is designed to provide guidance for the management of the association of the most beautiful villages of Russia, inhabitants of the most beautiful villages, rural tourism companies and local authorities in transition to the new experience economy approach accelerating the socioeconomic development of beautiful villages.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".