News media coverage of hurricane events and Caribbean tourism: a critical analysis of the last 40 years
Bibliographic record
Abstract
When making travel decisions, the news is a key source of weather information for tourists, particularly when there is a perceived risk of holiday disruption. Misrepresentation and sensationalist media coverage have been attributed to amplifying perceptions of climatic risk amongst the public. As the most tourism-reliant region in the world, how the media constructs and communicates hurricane events in the Caribbean can influence tourism demand, with implications for millions who rely on the sector for their livelihoods. Through a content analysis of global news articles published over the last 40 years (n = 635), this paper examines the attribute agenda setting and framing of hurricanes and Caribbean tourism. Over 60% of the articles omitted critical information (e.g., hurricane path, location of strike, category), with 11 of the 13 attributes negative in tone (i.e., risk amplifying). Four frames (victim, apocalyptic, disruptive, and business-as-usual) were identified, with most articles (66%) framing tourists and their vacation experiences as inevitable casualties of a hurricane event. The findings can aid regional stakeholder decisions on communication and marketing strategies during and following hurricane events to minimize negative impacts on tourism demand, particularly in unaffected destinations.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".