Mapping the impact of extreme weather on global events and mass gatherings: Trends and adaptive strategies
Bibliographic record
Abstract
Sport tourism, business events, festivals, and mass gatherings draw crowds that contribute to tourism and community development. However, extreme weather is disrupting events globally, exposing organisers and host destinations to risk as climate changes. This research analyses events affected by extreme weather in terms of location, purpose, and format using a global sample of 2,091 events reported as disrupted from 2004-2024. Qualitative insights are shared about the size, financial effects, and complexity of encounters, along with connections to extreme weather attribution research. While limited data prevents firm conclusions about global incidents, patterns related to crowds and trends over time in mature, English-speaking event economies (the US, UK, Canada, and Australia) are discussed, including the apparent increase in incidents across these countries. Overall, storms and their effects are found to be the most disruptive type of weather, however, there is regional variation. Arts, culture and entertainment events, particularly festivals and concerts, bear the brunt of impacts, followed by social and sporting events, although the picture varies among the four countries analysed. Findings help event organisers, destinations, and emergency managers better understand the specific weather risks that impact the timing, location, format, and viability of events, providing insights that can improve adaptation and resilience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".