Linkages between Climate Change and Coastal Tourism: A Bibliometric Analysis
Bibliographic record
Abstract
Tourism and climate are inextricably linked to several interactions. In recent years, there has been a greater focus on the linkages between climate change and coastal tourism. This study aims to provide an overview of the literature on climate change and coastal tourism, identify core areas of interest and important sources and authors, and examine the thematic evolution of the field. A bibliometric analysis of 92 documents related to climate change and coastal tourism published in the Web of Science Core Collection database was carried out. The analysis provides information on the most cited papers, most leading authors, the most productive countries, and the most leading institutions in this field. The study utilized the Visualization of Similarities Viewer program (VOS) to map author keyword co-occurrences, co-citations, and bibliographic coupling. The study showed that, with some fluctuations since 2008, the number of publications in this field had increased significantly. The most influential authors and most productive institutions are from the United States of America, England, Canada, and other European countries. The findings of this study will assist researchers conducting climate change and coastal tourism-related studies to understand which papers, academics, organizations, countries, and journals have a dominant influence on climate change and coastal tourism research.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.123 | 0.175 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".