Tourism-related Climate Change Perspectives: Social Media Conversations about Canada’s Rocky Mountain National Parks
Bibliographic record
Abstract
This study employed quantitative social media big data analysis in conjunction with qualitative analysis of postings to better comprehend online lay discourse of climatic change issues in a nature-based tourism destination, Jasper National Park, Canada. Such mixed methodological approaches to big data enable tourism researchers to not only study unstructured social media big data for future-proofing purposes but to address some methodological concerns often raised about solely using corpus linguistic or thematic analyzes. This study unearthed divergent themes regarding tourists’ perceptions of climate change upon visiting JNP, with the most significant discourses on climate grief, education and interpretation, pro- environmental behaviors, and last-chance tourism. It was also observed that despite scientific links between increasingly intense and extended wildfire seasons and climate change, visitors failed to connect wildfire’s negative impacts on visitors’ experiences in Canada’s Rocky Mountain national parks with climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".