Analysis of Climate Change and Tourism Related Studies with Visual Mapping Technique
Bibliographic record
Abstract
Climate change is a subject discussed by researchers in a wide range of aspects. This study aims to investigate the situation of climate change and tourism-related research in the international tourism literature. In doing so, the study employs the document review method within the scope of descriptive analysis. In this context, the study chooses the Web of Science database as the research area, and the visual maps are utilized with the help of Citespace and VosViwer software. Scanning the words climate change and tourism from the Web of Science database, the study considers 393 articles published between 1998-2022 (May). Thus, its research sample consists of 393 articles. Evaluating the study's findings coherently, the study reveals that research published within this context gains momentum, particularly in 2007, while 2011 is the year with the most publications. Within the institution-based evaluation, the League of European Research Universities Leru and the University of Waterloo are the two universities with the highest publication rate on the topic. According to the distribution of WOS categories, the Hospitality Leisure Sport Tourism category has been the field in which most research is published. Besides, the articles are predominantly published in the Journal of Sustainable Tourism. The Social Sciences Citation Index is the index in which the articles are frequently scanned. According to the analysis of collaboration among authors, Scott, Hall, Becken, and Gossling are the pioneer researchers on the subject. On the other side, Citation Explosion Model points out that the most cited researcher is Becken. While the researcher who published on this subject on his own is Scott, the most cited single authors are Elsasser and Burki. Ranking the countries in their publication number among countries, Australia, the United States of America, and Canada are shown to be the most prosperous countries, respectively. When examining the entire sample set of research in terms of keywords, the study shows climate change is the most frequently used keyword.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".