Residents’ Perceptions toward Tourism Development: A Case Study from Grand Canyon National Park, USA
Bibliographic record
Abstract
Although the impacts and challenges of tourism in towns and cities near protected areas have been studied extensively, there is a lack of both data and understanding that limits progress towards generalizable solutions, planning strategies, and guidance for addressing the increasing pressures affecting these communities. This article compares the factors influencing residents’ perceptions and local support of tourism in five gateway communities to Grand Canyon National Park. Importance–performance analysis (IPA) and structural equation modeling (SEM) were used to assess the proposed measurements of perceptions and hypotheses concerning local support and to compare the relationships among selected variables, such as community participation (CP), living environment (LE), trust in tourism institutions (TT), tourism benefits (TB), community satisfaction (CS), and perceived tourism cost (TC). Four groups of factors influenced residents’ perceptions; these were classified into four stages based on their management priority. A gap between the desires of community residents for the development of national parks and community tourism and the current state of development was identified, suggesting that these communities would benefit from management measures to mitigate the impacts of tourism. Through SEM, five factors were verified as drivers of local support for national park tourism development, including community participation, living environment, trust in tourism institutions, tourism benefits, and community satisfaction. Perceived tourism cost was not found to be a significant driver.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".