The Impact of Wine Tourism Business: Case Study of Newfoundland Wineries
Bibliographic record
Abstract
Wine tourism has been defined as the visiting of vineyards and wineries where wine tasting and purchase of the wines are the main purpose of the visit. Over the past quarter-century, Canadian vintners have increased their production of high-quality wines. Although Canada is not a major wine producer by global standards, the industry has evolved into a niche maker of internationally-respected ice wines and late harvest wines due to cool-climate influences. The study of wine tourism and their management offers many opportunities to reflect on the importance of sustainability and the possibilities of implementing new tourism approaches in a new direction in the province. Newfoundland province has only two wineries and could be a major player in this type of tourism. However, literature of wine tourism in the province is lacking even though the results in this study reveal there is sufficient interest and knowledge of wine tourism industry. Hence, this research has attempted to conceptualize the growth of wine tourism products, experiences, impacts and their management in the Newfoundland region. The research which adopts both quantitative (surveys) and qualitative approaches (interviews and participatory approaches) examines the potential impact of wine tourism in Newfoundland and how wine tourism is being managed based on the two distinct case studies in the Province. The findings of this research therefore have implications for wine tourism development and promotion in the Province, in Canada and internationally. As with all research, this study had some limitations which will serve to identify and future research needed. Since the data was collected through purposive sampling approach, it would be suggested that any generalizability beyond this context of study be used with caution.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".