Global wine tourism: research, management and marketing
Bibliographic record
Abstract
Introduction, J Carlsen and S Charters Section 1: The Wine Tourism Setting * Do Tourism and Wine Always Fit Together? A consideration of business motivations, R Fraser and A Alonso, Lincoln University, New Zealand * Land Use Policy and Wine Tourism Development, P Williams, Simon Fraser University, Canada, K Graham, Business Council of British Columbia and L Mathias, Canadian Cancer Society * Enhancing the Wine Tourism Experience: The Customer's Viewpoint, L Roberts, Victoria University, Melbourne and B Sparks, Griffith University, Australia Section 2: Wine Tourism and Regional Development * Wine Tourism and Sustainable Development, J Gammack, Griffith University, Australia * Emerging Wine Tourism Regions: Lesson for Development, B Sparks and J Malady, Griffith University, Australia * Determinants of Quality Experiences in an Emerging Wine Region, T Griffin and A Loersch, University of Technology Sydney, Australia Section 3: Wine Marketing and Wine Tourism * Influences on post-visit wine purchase (and non-purchase) by new Zealand winery visitors, R Mitchell, University of Otago, New Zealand * Electronic Marketing and Wine, J Murphy * Understanding the impact of wine tourism on post-tour purchasing behaviour, B O'Mahony, Victoria University, Australia, J Hall, L Lockshin, University of South Australia, L Jago, Victoria University, Australia and G Brown, University of South Australia Section 4: The Cellar Door * Wine tourists in South Africa: a demographic and psychographic study, D Tassiopoulos and N Haydam * Younger Wine Tourists: A study of generational differences in the cellar door experiences, S Charters and J Fountain, Edith Cowan University, Australia * The effects of survey timing upon visitor perceptions of cellar door quality, M O'Neill and S Charters Section 5: Wine Festivals and Events * Wine Festivals and tourism - a triangulated approach to festival satisfaction and quality, R Taylor, Curtin University, Australia * Wine festival: Analyses for attendees' motivational segmentation, and the event's promotional effects, J Yuan, Texas Tech University, USA, S C Jang, A C Liping and A M Morrison, Purdue University, USA and S Linton, Indiana Wine Grape Council, USA * A Strategic Approach to Wine Festival Development: The case of the Margaret River Wine Festival, J Carlsen and D Getz, University of Calgary, Canada Section 6: Wine Tours and Trails * Nautical wine tourism: A Strategic Plan to Create a Nautical Wine Trail in the Finger Lakes Wine Region of New York State, M Q Adams, University of Adelaide, Australia. * Wine Routes in Portugal, L Correia, Leiria Institute Polytechnic, Portugal and M Passos Ascencao, HAAGA University of Applied Sciences, Finland * Are we there yet? How to navigate the wine trails, D Hurburgh, Myriad Research Associates, Australia and D Friend Summary and Conclusions * The Future of Wine Tourism Research, Management and Marketing, S Charters and J Carlsen.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.010 |
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".