Bibliographic record
Abstract
Purpose Tourism faces a range of interconnected and potentially transformative global risks – collectively considered an evolving polycrisis – that have not been adequately defined and understood. As a result, the industry struggles to proactively anticipate and mitigate potential future challenges, while governments lack insight for strategic longer-term decision-making on tourism development. The purpose of this paper is to advance tourism sector consideration of global change threats and their complex interactions and more effectively incorporate these risks into tourism futures planning. Design/methodology/approach This conceptual gap is addressed through a discussion of the World Economic Forum’s Global Risk Reports, and their definition of environmental, economic, geopolitical, societal and technological risk categories. In applying results to tourism, a preliminary expert assessment of global risks serves as a foundational framework to incorporate potential global change threats more effectively into tourism futures planning and decision-making. Findings Additional research should be prioritized to examine global risks most influential of tourism, how and where they may interact, how to convert risk categories into measurable indicators and to evaluate whether risk assessments can contribute to mitigating the evolving polycrisis. Originality/value This paper discusses the systematic and strategic engagement with global risks for tourism, critically reviews the World Economic Forum’s Global Risk Reports from tourism perspective, presents key risk dimensions driving future tourism development, provides a foundational framework to further assess global risk for tourism and compels tourism academy to prioritize global change research agenda.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".