Assessing Local Perceptions of Sustainable Tourism Development in National Parks: An Analysis Using SUS-TAS
Bibliographic record
Abstract
Sustainable tourism has been recognized as a critical driver for enhancing both economic resilience and environmental stewardship in protected areas.To evaluate the perceptions of local communities regarding the impacts of sustainable tourism, a quantitative assessment was conducted across four national parks located in the Western Balkans: Bjeshkë t e Nemuna National Park (Kosovo), Prokletije National Park (Montenegro), and Valbona and Theth National Parks (Albania).The validated Sustainable Tourism Attitude Scale (SUS-TAS) was employed to measure resident attitudes across seven conceptual dimensions, operationalized through a 44-item questionnaire.A total of 595 responses were obtained from residents in the target regions, with stratified samples from Kosovo (n = 325), Albania (n = 160), and Montenegro (n = 110).Statistical analyses were performed using one-way analysis of variance (ANOVA) to identify significant differences among the national subgroups, followed by Scheffé post-hoc tests to specify intergroup contrasts.Findings revealed significant divergences in resident perceptions regarding the benefits and challenges associated with sustainable tourism, indicating a heterogeneous understanding of its value and impact.These differences suggest the necessity for region-specific strategies and collaborative policy frameworks aimed at enhancing awareness, fostering local engagement, and promoting equitable participation in environmental conservation and tourism planning.The results underscore the critical role of community-based perspectives in achieving long-term sustainability objectives within national park governance and highlight the importance of harmonizing cross-border efforts to strengthen sustainable tourism development in transboundary protected areas.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".