Agritourism: A Bibliometric Insight into Sustainability and Rural Development
Bibliographic record
Abstract
Agritourism has increasingly attracted scholarly interest due to its potential to foster sustainable rural development.However, related studies are scattered and lack coverage of study trends and topics.The study focuses on the analysis of the keyword trends and structures of topics in the field with the help of Scopus and Web of Science data.A total of 826 peerreviewed articles were analyzed, demonstrating that each article had an average of 14.4 citations, an annual publication growth rate of 17.29%, and an international co-authorship rate of 9.09% over time.Bibliometrix (R package) was used to conduct keyword co-occurrence analysis, co-authorship network, and thematic evolution mapping.These findings show that four thematic clusters are outlined, such as sustainable development and rural spaces, ecosystems and agriculture, governance and policy frameworks, and visitor experience and marketing.These groupings include important areas of rural tourism, conservation of biodiversity, response to climate change, agricultural policy, innovation in rural development and cultural experiences of the visitors.Furthermore, co-authorship analysis highlighted important clusters of international collaboration, with Italy (103 articles, 2848 citations) and China (175 articles, 2664 citations) emerging as the leading contributors.The analysis of the evolution of the topic shows that the research on agritourism has gradually shifted from traditional topics such as rural tourism and sustainable development to emerging topics such as circular economy, social innovation and public health.The results provided a landscape of agritourism, the interdisciplinary and international cooperation aspects, gaps and research opportunities on long-term tourism development.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.141 | 0.229 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".