Nature-based tourism as therapeutic landscape in a COVID era: autoethnographic learnings from a visitor’s experience in Iceland
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it