Who, What for Whom IAST Scholars Publish?—A Bibliometric and Science Mapping Analysis of Leading Tourism Scholars
Bibliographic record
Abstract
Background: The International Academy for the Study of Tourism (IAST) has undeniably contributed to tourism research. However, the evolution of its members’ research outcomes remains underexplored. Additionally, understanding the academic community’s focus is key to assessing its contribution to knowledge development. This paper, therefore, seeks to examine the scientific publications, publication trends, and metrics of IAST scholars. Methods: The publication patterns of ninety IAST scholars were systematically investigated through a bibliometric and advanced science mapping analysis. This research utilized VOSviewer and the Biblioshiny-R-Studio package for data processing and visualization. Results: This study uncovers dynamic publication trends over the last five years, marked by an acceleration in scholarly production from 2001 to 2012, with an anomalous decrease in 2010. These contributions are widely disseminated across leading academic journals, reflecting a significant intellectual influence through high citation indices and their role as foundational references. Thematically, these scholars consistently foreground central issues such as sustainable tourism development and the protection of vulnerable regions, encompassing cultural and natural heritage. The spectrum of investigated topics spans all levels—from global to local scales—with a multidisciplinary emphasis on tourism economics, governance, tourist consumer behavior, stakeholder roles, and the marketing and sustainability aspects of tourism. Conclusions: IAST scholars’ publications clearly demonstrated trends, impact, and significant terminology in tourism studies. Therefore, academic communities, among others, should broaden their focus, with IAST serving as an example of a community—where scholars produce knowledge-based from diverse perspectives.
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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 | 0.016 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.075 | 0.099 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".