A Bibliometric Analysis: Wine Tourism in the Sector
Bibliographic record
Abstract
The term "wine tourism" was first used in 90's from Australia.The definition of ecotourism has been studied by many academics throughout its history, reaching the conclusion that it is the activities carried out by people outside their usual environment in a given period of time related to the viticulture of the environment and wine.This study aims to enhance knowledge in the wine tourism field by employing bibliometric methods to quantitatively analyze its evolution over the past two decades.This analysis shows that from 2019 onwards, publications increased considerably, reaching a peak in 2020, mostly (linking with COVID-19 lockdown).In the areas of geography and economics, as research in the area of tourism is still very recent.The conceptual analysis shows the variety of terms used by researchers and how they have evolved over time, the most commonly used being "touristic" and "vineyard", with others appearing such as "meal" connected to "winery waste" or "tourism development", which are more akin to current management.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.114 | 0.233 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".