Hosting Regional Sport Events: Insights from Emerging Sport Tourism Destinations
Bibliographic record
Abstract
The aim of this exploratory study was to investigate and gain stakeholder insights into the nature of hosting sport tourism events and using them as a regional development strategy in two emerging sport tourism markets (i.e., regions that have recently begun to explore the potential of hosting small-scale sport events as a tourism development strategy). Specifically, the current research addressed five research questions in relation to tourism stakeholders’ input from the Waterloo and Niagara Regions in Ontario, Canada: (i) Why do destinations engage in sport tourism development strategies? (ii) What are perceived constraints to engaging in sport tourism events as development strategies? (iii) How do stakeholders decide which sport events to pursue, bid for and host? (iv) What are the perceived regional impacts of hosting sport tourism events? (v) What extent is leveraging these impacts considered in sport tourism strategy development? A total of 10 semi-structured interviews were conducted with key sport and tourism decision making stakeholders in the two regions during February and March 2015. A thematic analysis (Braun & Clark, 2006) was used, which included different phases of coding and analysis to derive key themes and concepts. Themes emerging from data included, Embracing the current sport tourism situation; Deriving benefits from hosting sport tourism events (e.g. job creation, sport development, volunteerism, enhanced well-being, destination image development, increased community engagement and destination differentiation); Overcoming challenges of hosting sport tourism events (e.g. facility and infrastructure constraints, resident irritation and displacement; resource allocation and navigating the political environment); Understanding regional impacts of hosting sport tourism events and Effective leveraging of sport tourism events. Implications for research and practice are further discussed in relation to each theme.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".