From a Wine Tourism Village to a Regional Wine Route: An Investigation of the Competitive Advantage of Embedded Clusters in Niagara, Canada
Bibliographic record
Abstract
With changes in growing techniques and trade regulations, the Niagara Region has become home to an emerging New World Wine Route, making it one of Canada's premier wine tourism destinations. The purpose of this paper is to investigate the competitive advantages of embedded clusters at three different scales along the Niagara Wine Route. Building on earlier concepts of growth poles and agglomeration economies, cluster theory focuses on competitive advantage and indicates new roles for companies, governments and other institutions to enhance competitiveness. At the smallest scale, a Wine Tourism Village is explored through Cave Spring Cellars, which dominates the Village of Jordan along with its premier restaurant and country inn. A small shopping district has opened on the main street next to the winery complex. Increasing in scale is the cluster of eleven wineries surrounding the heritage-shopping town of Niagara-on-the-Lake. Complementary to the wineries are close to 60 tourist shops, numerous accommodation establishments, a historic fort and a festival theatre. Finally at the Regional level, is the entire Niagara Wine Route with over 50 wineries, which are connected to the major tourist attractions of Niagara Falls. Key informant interviews were conducted with wineries at the Village, Town and Regional level. Nearest-neighbour analysis is performed on the entire Wine Route along with land use mapping in the three locations. A schematic diagram based on the embedded clusters is presented illustrating the importance of horizontal and vertical linkages in generating a competitive advantage through location in the context of cluster theory.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".