Orillia's Got Talent: Attracting Tourists with Nonstop Entertainment
Bibliographic record
Abstract
The cottage country in the Province of Ontario is a major tourist destination. Bringing tourists into a specific part of cottage country requires a unique attraction. When one thinks of Orillia, Ontario, for example, one thinks of author Stephen Leacock’s fictional town, Mariposa. When one thinks of Mariposa, one thinks of the Mariposa Folk Festival. While this is the key event in Orillia’s tourist program, it by no means stands alone. The Jazz Festival, the Blues Festival, the Beatles Celebration, and other musical events have been linked with art, drama, comedy, literary, and other cultural events to create an extended summer of tourist attractions. This paper discusses the development of this cultural tourism program and examines its success at drawing tourists to Orillia, contributing to the redevelopment of the downtown, and providing employment alternatives to a bygone industrial community. Keywords: urban tourism, cultural economy, economic development, downtown redevelopment
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".