Racinos in rural Canada: economic impacts of the Grand River raceway on Elora, Ontario, Canada.
Bibliographic record
Abstract
Facilities that combine electronic slot machines and pari-mutuel racetracks (i.e. racinos) have been introduced to rural North America to stimulate local economies and/or racetrack patronage. This study examines the economic impacts of one racino on the community of Elora, Ontario, Canada. Renowned as a heritage tourist destination, Elora experienced economic decline during the 1990s. A racino was constructed by the Grand River Agricultural Society in 2003 to offset this downturn and to promote increased visitation to the village. Using a variety of data published by the Ontario Lottery and Gaming Corporation and the Grand River Agricultural Society, we first estimate the impact of the facility on employment and revenue generation (primary impacts). Survey data, collected from local business owners, and visitors to the historic downtown, are then used to describe and explain the perceived impacts of racino patrons on the economic well-being of local firms (secondary impacts). Recommendations to increase the facility's impacts are then drawn from comments provided by key informants. Our analysis finds that the facility has generated not only employment and revenue, but also sponsorships and in-kind financial benefits. We find, however, that secondary impacts are minimal, with few businesses perceived to benefit from patron expenditures. We attribute this to visitors' motivations and origins, and spatial placement of the facility. Four recommendations are provided to attract gamers and their partners to the shopping district. It is advised, however, that these actions be undertaken with caution to ensure retention of the historic ambiance that draws the heritage-seeking consumer. Keywords: racino, pari-mutuel racetrack, heritage tourist destination, historic ambiance, rural regeneration, visitation
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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 | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".