Nature-based tourism as therapeutic landscape in a COVID era: autoethnographic learnings from a visitor’s experience in Iceland
Bibliographic record
Abstract
Abstract One of the few silver linings in the COVID pandemic has been a new appreciation for, interest in, and engagement with nature. As countries open, and travel becomes accessible again, there is an opportunity to reimagine sustainable nature-based tourism from a therapeutic landscape lens. Framed within the therapeutic landscape concept, this paper provides an autoethnographic account of a visitor’s experience of three different natural landscapes in Iceland shortly after the country’s fourth wave of the pandemic. It adds to the understanding of the healing effects of the multi-colored natural landscapes of Iceland. The natural landscapes of interest herein include: the southern part of the Westfjörd peninsula, Jökulsárlón glacial lagoon, and the Central Highlands. In totality, the natural, built and symbolic environments worked in synchronicity to produce three thematic results: restoration, awe and concern, all which provided reduced stress, renewed attention, as well as enhanced physical and psycho-social benefits for the autoethnographic visiting researcher. Implications of these restorative outcomes for sustainable nature-based tourism in a post-COVID era are discussed. This paper highlights how health and tourism geographers can work collaboratively to recognize, protect, and sustain the therapeutic elements of natural landscapes, recognized as a cultural ecosystem service. In so doing, such collaborations can positively influence sustainable nature-based tourism development and consumption through proper and appropriate planning and development of such tourism destinations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".