Toward Resilient Nature-Based Tourism in the Post-Pandemic Era: Integrating Governance, Visitor Dynamics, Finance, and Ecosystem Integrity
Bibliographic record
Abstract
Nature-based tourism (NBT) now underwrites global conservation ambitions, rural livelihoods, and emerging climate-mitigation pathways, yet the COVID-19 shock exposed its structural vulnerabilities. This editorial curates eight articles that collectively advance a systems view of NBT resilience, integrating adaptive governance, visitor experience redesign, and equitable finance with ecosystem integrity. Methodological pluralism—ranging from critical ethnography to footprint dashboards—reveals convergent evidence: diversified revenue, participatory governance, and affect-based visitor stewardship are mutually reinforcing levers. We position these insights within the Kunming–Montreal Global Biodiversity Framework, the UNWTO recovery agenda, and IUCN Resolution 130, and outline a transdisciplinary research agenda to transform NBT destinations into global resilience infrastructures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".