Visitor outcomes from dark sky tourism: a case study of the Jasper Dark Sky Festival
Bibliographic record
Abstract
Dark sky tourism is a growing, but under-studied sector of ecotourism. While some research has examined the economic impacts, regional sustainability, and management of dark sky tourism, researchers know little about tourist experiences and outcomes. This study seeks to determine visitor outcomes (satisfaction, learning, attitudes, and behavior changes) among participants at the Jasper Dark Sky Festival in Alberta, Canada. Visitors were middle-aged, balanced between genders, traveled an average of 430 km, and were primarily urban-based. Most were first-time visitors, were present 2 or more days, and attended over 5 festival events. Respondents reported high satisfaction levels, due to the low cost and diversity of events, and welcoming nature of the community, presenters, and volunteers. Respondents reported many areas of learning, particularly about the night sky and night animals. Respondents had very positive attitudes about protecting dark skies, but only 42% planned to change any behaviors to protect dark skies. Study results will help festival organizers design dark sky tourism events to optimize visitor outcomes. In particular, festival organizers can address some barriers to behavioral change, such as stressing the value of dark skies and providing information about how to reduce light pollution.
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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 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".