A call for reducing tourism risk to environmental hazards in the Himalaya
Bibliographic record
Abstract
As mountain tourism rapidly expands in remote landscapes, there is a critical need for improved disaster risk management to ensure the safety of tourists and industry workers, safeguard infrastructure designed to support tourism and service industries (e.g., transportation), as well as protect the local economies that have come to depend on tourism revenue. Drawing from recent disasters in the Himalaya, we present evidence that the promotion of safe and sustainable tourism is out of sync with the proliferation of inbound tourists who are prone to many types of environmental hazards. The key driver of this situation is commercialisation. Other factors include increased mobilities/access of tourists who are often unaware of or ill-prepared to cope with hazards; lack of regulations with respect to overcrowding, safety and building codes increased exposure to climate change phenomena; and limited disaster response capabilities, including responsibility at the local level. In this perspective we argue that this particularly complex situation is best addressed through the lens of a dynamic system, whereby strong leadership, increased regulation of access and participation, and enhanced professionalism via training are key leverage points in countering uncontrolled commercialisation that drives increased risk to known hazards. The inclusion of tourism into disaster risk management systems is also needed where hazard risks and tourist traffic are high, as tourists are part of the transient population who are often unfamiliar with local conditions and ill-prepared to cope with extreme adversity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".