Sustainable Leisure Landscapes in Icelandic Rural Communities: A Multidisciplinary Approach
Bibliographic record
Abstract
The rapid increase of tourism in the Arctic highlights the critical importance of holistic planning of land use for tourism to ensure that recreational activities are only developed where natural and cultural environment is able to sustain their impact. This paper aims to devise a holistic plan for use of land for Icelandic tourism based on landscape analysis and stakeholders’ perceptions, and to critically discuss the role of holistic approaches and zoning principles in such planning with regard to different market groups of visitors. A case study was conducted in Skaftárhreppur municipality in southern Iceland. Geographical information systems (GIS) were used to analyse its suitability for the various forms of recreational activity based on stakeholders’ perceptions, landscape sensitivity, the area’s recreational opportunity spectrum (ROS), and visitor type according to the purism scale model. The results reveal a lack of balance between the area’s current recreational use and its landscape sensitivity, something which reflects many of the negative aspects of the exponential growth within Icelandic tourism which has taken place over the past decade. The results further stress the importance of appropriate infrastructure to channel the increasing flow of mass tourism and to direct this flow to carefully chosen focal points. In seeking to develop site-specific zoning for the different market groups, focal points have proven to be a critical management tool. By controlling the number, type and location of visitors, their flow is regulated, and thereby the impact of tourism is managed. The use of well-defined focal points in the zoning procedures will reduce the environmental and social pressure from tourism, reduce the cost of maintaining infrastructure incurred by communities, ensure visitor satisfaction, and protect the most sensitive areas from overexploitation by tourism.
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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 | 0.005 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".