Exploring the Role and Opportunity of the Visitor Economy for Main Streets in Canada
Bibliographic record
Abstract
Main streets are community hubs of economic and cultural activity, often represented by a business organization tasked with improving the experience and environment through place making and management, and marketing to attract visitors. These responsibilities often overlap with the functions and roles of tourism destination organizations but on a more local level. The purpose of this study, therefore, is to advance conceptualizations of the visitor economy and main streets as destinations to further understand their role and potential within tourism destination frameworks. This qualitative exploratory study involved a thematic analysis of semistructured interviews with 36 representatives of Canadian main street organizations [Business Improvement Areas (BIAs)]. Findings demonstrate that visitor economy engagement is part of the work and function of main street organizations, and that main streets are both destinations in their own right and components of wider regional tourism systems. COVID-19 created immediate and potentially sustained demand for local travel and lad to the establishment and strengthening of partnerships between organizations representing communities at different scales. Incorporating main streets into the wider destination ecosystem could help maximize visitor economy opportunities benefiting both the local and wider destinations. This study contributes to the conceptual understanding of main streets as destinations, and the visitor economy. Main streets and the organizations that represent them are positioned as key actors in the management and marketing of destinations, a topic receiving limited attention in the literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".