When Severe Weather Becomes a Tourist Attraction: Understanding the Relationship with Nature in Storm-Chasing Tourism
Bibliographic record
Abstract
Abstract Since the mid-1990s, tourists can purchase storm-chasing tours to observe dangerous, potentially deadly natural phenomena—that companies cannot guarantee will occur. This context calls for a better understanding of the core aspect of the relationship between storm-chasing tourism and severe weather. What interest in severe weather spurs people to embark on storm-chasing tourism? How do they deal with severe weather becoming a tourist attraction through storm-chasing tourism? The present exploratory study investigates these questions using a qualitative methodology. It first examines the ways severe weather is depicted in participants’ discourse and on storm-chasing companies’ websites, illustrating they are multiple and intersecting. It then describes the various rationales used by tourists, guides, and owners when they discuss storm-chasing tourism turning severe weather into a tourist attraction, showing how the activity contributes to nature’s commodification process. Seeking to provide an initial anthropological interpretation of the findings, this study suggests that storm-chasing tourism brings together acceptance and exploitation of nature. Indeed, severe weather appears to be sought for its power over humans while also being marketed as an ordinary commodity. Albeit preliminary, this study sheds light on a fundamental feature of storm-chasing tourism that researchers have not yet fully addressed and enhances the comprehension of a piece of humankind’s relationship with nature in current Western societies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".