A Framework of Sustainability Performance Indicators for Terrestrial Tourism Development: An Architectural Perspective
Bibliographic record
Abstract
Terrestrial nature-based tourism offers economic and empowerment benefits but also poses risks degrading ecosystems, biodiversity, and local cultures when planning tools lack contextual sensitivity.This study develops a concise set of sustainability performance indicators for terrestrial nature-based tourism from a sustainable architectural perspective.Using a mixed-method descriptive-explanatory approach, the research identified and classified 75 performance indicators into four dimensions aligned with Minister of Tourism & Creative Economy Regulation Number 9 of 2021: environmental (planet), socio-cultural (people), tourism management (management), and socio-economic (prosperity).These indicators complement and extend the Global Sustainable Tourism Council (GSTC) guidelines by addressing gaps specific to terrestrial nature-based tourism in Indonesia.The results provide a practical framework for destination managers, planners and architects, policymakers, auditors, and researchers, fostering tourism development that is more responsible, architecturally informed, and grounded in local contexts.Nevertheless, the 75 performance indicators still require expert validation to determine which criteria and indicators should be prioritized in advancing sustainable terrestrial nature-based tourism.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".