Discourses of Fear in Online News Media: Implications for Perceived Risk of Travel
Bibliographic record
Abstract
This paper analyzes the role of Canadian online news media in framing travel during the pandemic. The article applies Altheide’s concept of the problem frame to reflect how news media contribute to the emergence of a highly rationalized problem that, in turn, generates a discourse of fear. While the impacts of COVID-19 on tourism have been extensively examined within tourism scholarship, less attention has been devoted to the impact of news media. Because travel and the pandemic are heavily intertwined, discourse analysis can help process media narratives, furthering our understanding of their role in influencing perceived risk of travel. A critical discourse analysis of over 100 online news articles was conducted using thematic analysis to uncover themes in Canadian media sources and to explore how the media have framed travel during the pandemic. The role of online news media in promoting fear was communicated through the themes of anxiety, antitrust, avoidance, and animosity. The role of the media in producing the problem frame in the context of travel was examined as well as its implications for perceived travel risk and tourism demand. The power dynamics between media, government, and the citizens it serves are also discussed.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.016 | 0.025 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".