Trails of Transformation: Balancing Sustainability, Security, and Culture in DMZ Walking Tourism
Bibliographic record
Abstract
This study examines rural walking tourism as a sustainable strategy for revitalizing regional economies and preserving natural environments, focusing on the DMZ Punch Bowl in South Korea. Although rural walking tourism has been widely promoted for sustainability, little is known about its operation in geopolitically sensitive and militarized ecological zones, such as the Korean DMZ. Adopting the qualitative case study method, we explored three essential conditions for sustainable rural walking tourism: environmental friendliness, experiential immersion and sense of place, and local economic revitalization through stakeholder cooperation. We employed a hybrid thematic analysis using inductive and deductive coding to analyze the triangulated data collected from interviews, field observations, and policy documents. In-depth interviews with ten walking tourism experts revealed that storytelling that emphasizes local history, ecological conservation, and unique cultural identity enhances tourists’ emotional attachment and sense of place immersion. The DMZ Punch Bowl case was selected due to its effective integration of these elements, achieved through a collaborative governance structure involving government agencies, military units, and local communities. The findings highlight that coordinated management and stakeholder cooperation are crucial for balancing land use policies, ecological preservation, and tourism safety. Additionally, walking tourism significantly contributes to local economic growth through direct spending, job creation, increased resident incomes, the sale of local specialties, and participation in experiential activities. This study provides valuable insights and a replicable model for sustainably developing walking tourism in similarly sensitive or ecologically significant rural areas.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".