Lake Watershed Tourists: Who They Are and How to Attract Them
Bibliographic record
Abstract
Lakes act as both ecosystems for numerous life forms, and in many cases, tourist destinations. In order to sustain lakes as tourist destinations and protect them as ecosystems, municipalities need to understand the tourist, their demographics, motivations, satisfaction levels and the tourists’ desire for sustainability initiatives. To this end, the purpose of this study was to examine lake tourists, their demographics, motivational drivers and their relation to each other. Using a sample of 475 surveys, cross-tabulations, t-tests, and ANOVA’s were executed to understand differences and relationships. The results show varying differences with three key findings. First, motivations drive visitors based on their age and gender. Second, income has an influence on the importance visitors put on businesses promoting sustainability, such that the greater the income-level the lower the importance visitors placed on businesses promoting sustainability. This research builds upon past segmentation studies to show the link between sustainable types of tourism and the importance of sustainability. This adds a third key finding to the area of inquiry, demonstrating that increased awareness not only strengthens the relationship between demand and nature-based offerings, but that it can also increase satisfaction levels.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".