Effects of Social Media on Enotourism. Two Cases Study: Okanagan Valley (Canada) and Somontano (Spain)
Bibliographic record
Abstract
The aim of this article is to analyze the social media effects on enotourism. Two territories of similar extension and with historical coincidences in their development have been selected: the Okanagan Valley, Canada, and the region of Somontano, Spain. Methodologically, an analysis of the content on Twitter has been performed, collecting 1377 tweets. The conclusion is that wineries create sentimental and experiential links with the users, avoiding commercial communications. Specifically, Okanagan wineries establish a relevant conversation network on Twitter based on the high percentage of responses, which is 31.3%, but this is not so in the case of Somontano, which is 12.8%. The tourist attractions most used to create a bond are the wine landscape and the gastronomy in the case of both territories. The tourism sustainability variable remains a minor matter in the emission of messages on Twitter.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".