Outdoor tourism to escape social surveillance: health gains but sustainability costs
Bibliographic record
Abstract
We analyse motivations and perspectives of outdoor tourists and tourism stakeholders in the Islamic Republic of Iran. We use semi-structured qualitative interviews, and interpretivist grounded theory, with basic and axial coding and fine-scaled differential narrative analysis. We distinguish three principal tourist segments, seeking: exhilaration through adventure; enjoyment of nature; and escape from cultural restrictions and associated social surveillance. The nature and adventure segments behave as ecotourists, and gain improved eudaimonic wellbeing. Nature tourists gain psychological restoration through calm and tranquil nature contemplation. Adventure tourists gain psychological recharge through challenge and achievement. The escape segment, in contrast, aims for hedonic wellbeing, is heedless of its social and ecological impacts, and does not behave as ecotourists. It adopts an ecotourism disguise, to avoid being observed as it flouts expected cultural norms. It uses unauthorised and clandestine logistics providers, creating substantial management obstacles for authorised commercial outdoor tour providers. Temporary escape from social surveillance generates mental health gains as a psychological safety valve for the tourists concerned, but their behaviour imposes unsustainable costs on local communities, natural environment, nature and adventure tourists, and outdoor tourism operators. These costs reduce the net social economic gains achieved from the mental health benefits of outdoor 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".