Bibliographic record
Abstract
Research on wine tourism has expanded rapidly since the early 1990s with approximately two thirds of the literature coming from Australia and New Zealand, countries with not only substantial wine tourism but also a long record of wine marketing research. Of the remaining literature the dominant source countries for research are Canada and the US. Seven themes are identified from the literature and are discussed in turn: the wine tourism product and its development; wine tourism and regional development; the size of the winery visitation market; winery visitor segments; the behavior of the winery visitor; the nature of the visitor experience; and emerging area of research on the biosecurity risks posed by visitors. For each of the themes future research challenges and issues are identified. The review concludes by noting that although there is now a significant catalogue of research in the field, methods are still relatively crude and studies still tend to be regionally focused and quite generic in nature. There is therefore a need not only to improve the means by which results from different locations and populations can be compared but also to employ greater sophistication in the employment of qualitative and quantitative techniques in their examination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.037 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".