Testing the Implications of an Integrated Rural Tourism Framework for the Niagara Wine Region
Bibliographic record
Abstract
Tourism, in general, can contribute to and integrate with rural economies for rural development through industry associations and community participation (Saxena et al., 2007), as well as act as a storehouse for “natural and historical heritage” (Lane, 1994, p. 103). As realization that tourism can benefit local areas increases, so too has the discussion around tourism as a tool for rural areas. In 2007, building on the concept of Integrated Rural Development (IRD), Saxena et al. first discussed the concept of Integrated Rural Tourism (IRT). IRT was suggested as an approach to understanding the complexities of rural tourism through an examination of seven components (networking, scale, endogeneity, sustainability, embeddedness, complementarity, and empowerment), and as a means for exploring the ability of tourism to produce benefits for the rural area. \n \nIn the past, IRT has been used to examine how tourism has aided rural development in Europe and the US; however, its use in Canada, and more specifically the Niagara Peninsula, has yet to be realized. Using the Niagara Peninsula Appellation (NPA), the largest wine region in Ontario and Canada, as the case study, this project involved interviewing 17 wineries and five industry associations, in an attempt to answer two specific questions: (1) how does the wine industry and wine tourism aid in the development of Niagara’s rural area using the IRT concept, and (2) how can IRT aid in rural development through direct, experiential, conservation, development, and synergistic benefits. \n \nWhile there is still work to be done to improve upon tourism’s positive impacts in Niagara and its peripheral rural areas more generally, this dissertation has found that wine tourism has produced direct, experiential, conservational, and synergistic benefits for the Niagara Region. While there were also some developmental benefits, there is greater need for community engagement and improved industry synergy. \n \nFurthermore, this dissertation has found that the concept of IRT provides a reasonable framework through which to analyze the ability of wine tourism to benefit rural areas, although the addition of a focus on the marketing efforts and future goals of the area are needed.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".