A PANORAMIC VIEW OF INTERNATIONAL ARTICLES ON ECOTOURISM WITH VISUAL MAPPING TECHNIQUE
Bibliographic record
Abstract
The purpose of the research is to determine the trend and development of ecotourism over a 34year period (1990-2024) in the international literature.For this purpose, the bibliometric analysis method, which is one of the quantitative research methods, has been used in the study.The data regarding the 9243 articles (as of 09.05.2024) that contain the concept of ecotourism in the titles, keywords, and abstracts, were retrieved from the Scopus database.In the analysis of the data, Biblioshiny, a web interface provider application and open source software design for Bibliometrics R-package (R-Studio software), bibliometrics library, and bibliometrics have been used in accordance with certain parameters.When some of the findings that came out as a consequence of the analysis have been analyzed, it can be observed that there are 9243 articles in 1756 different journals, the most of articles are multi-authored, the most articles were published in 2023, the journal that published the most articles is Journal Of Sustainable Tourism, the authors are Jiekuan Zhang and Yurong Zhang and the affiliation is Griffith University.Furthermore, in the author collaboration network analysis, it has been found that 8 different clusters have been formed.When the collaboration among countries has been considered in terms of the emergence of articles on ecotourism, it has been found that China, United Kingdom, USA, Canada are the ones that have the highest level of collaboration with other countries.It is thought that the results of this study are important in terms of visually mapping the development process of research on the international level and conveying it to the reader and shedding light on the studies to be conducted in the future on this subject.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.034 | 0.040 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".