Sustainable tourism research towards twenty-five years of the journal of sustainable tourism

© 2018 Advances in Hospitality and Tourism Research. All Rights Reserved. The Journal of Sustainable Tourism (JOST) is a main journal in 'Geography, Planning and Development'. The concept of sustainable tourism has gained importance over time. This paper presents a general overview of the journal over its lifetime by using bibliometric indicators. Moreover, in order to establish the position of sustainable tourism research, this paper identifies the trends in research through bibliometric studies. It uses the Scopus database to analyse the bibliometric data. This analysis includes key issues such as the publication and citation structure of the journal; the most cited articles; the leading authors, institutions, and countries in the journal; and the keywords that are most often used. This paper also uses the visualization of similarities to graphically map the bibliographic material. This analysis provides further insights into how JOST links to other journals and how it links researchers across the globe. These results indicate that JOST is one of the leading journals in the areas where the journal is indexed, with a wide range of authors from institutions and countries from all over the world publishing in it. The results of the current study can provide insights into topics related to sustainable tourism that can be researched in the future

Large-Scale Analysis of the Accuracy of the Journal Classification Systems of Web of Science and Scopus

Journal classification systems play an important role in bibliometric analyses. The two most important bibliographic databases, Web of Science and Scopus, each provide a journal classification system. However, no study has systematically investigated the accuracy of these classification systems. To examine and compare the accuracy of journal classification systems, we define two criteria on the basis of direct citation relations between journals and categories. We use Criterion I to select journals that have weak connections with their assigned categories, and we use Criterion II to identify journals that are not assigned to categories with which they have strong connections. If a journal satisfies either of the two criteria, we conclude that its assignment to categories may be questionable. Accordingly, we identify all journals with questionable classifications in Web of Science and Scopus. Furthermore, we perform a more in-depth analysis for the field of Library and Information Science to assess whether our proposed criteria are appropriate and whether they yield meaningful results. It turns out that according to our citation-based criteria Web of Science performs significantly better than Scopus in terms of the accuracy of its journal classification system

Research networks and scientific production in Economics, The recent Spanish Experience.

This paper studies Spanish scientific production in Economics from 1994 to 2004. It focuses on aspects that have received little attention in other bibliometric studies, such as the impact of research and the role of scientific collaborations in the publications produced by Spanish universities. Our results show that national research networks have played a fundamental role in the increase in Spanish scientific production in this discipline.Bibliometric techniques, scientific production in Economics, research networks.

A BIBLIOMETRIC ANALYSIS: RESEARCH OF URBAN FARMING IN INDONESIA PERIOD 1991-2023

This research is related to urban farming as a major variable in this publication. The aim of this research is to investigate the profile of original scientific articles along with reviews on the topic of urban farming in Indonesia in the period 1991-2023 using bibliometric analysis. Journals related to urban farming in Indonesia published between 1991-2023 are taken from Scopus. The records analyzed and taken from the research material as characteristic of the subsequent quotation containing the distribution of the author's name, year of publication, principal author institution, publisher processed using Microsoft Excel 2016 and VOSviewer v.1.61 are used to create bibliometric diagrams. A total of 81 journals published in Scopus were written by 160 identified authors. The number of published articles continued to increase from 1991 to 2023, with the majority of articles written in English. The most cited article is Aquaculture Research with a 10-year quotation. Visualization analysis based on the accuracy of connected words in titles and abstracts has revealed several groups of research. This research contributes to providing a systematic overview of the productivity and visibility of research projects focused on urban farming in Indonesia, which is expected to be used to organize and prioritize future research.

A machine learning taxonomic classifier for science publications

Dissertação de mestrado integrado em Engineering and Management of Information SystemsThe evolution in scientific production, associated with the growing interdomain collaboration of knowledge and the increasing co-authorship of scientific works remains supported by processes of manual, highly subjective classification, subject to misinterpretation. The very taxonomy on which this same classification process is based is not consensual, with governmental organizations resorting to taxonomies that do not keep up with changes in scientific areas, and indexers / repositories that seek to keep up with those changes. We find a reality distinct from what is expected and that the domains where scientific work is recorded can easily be misrepresentative of the work itself. The taxonomy applied today by governmental bodies, such as the one that regulates scientific production in Portugal, is not enough, is limiting, and promotes classification in areas close to the desired, therefore with great potential for error. An automatic classification process based on machine learning algorithms presents itself as a possible solution to the subjectivity problem in classification, and while it does not solve the issue of taxonomy mismatch this work shows this possibility with proved results. In this work, we propose a classification taxonomy, as well as we develop a process based on machine learning algorithms to solve the classification problem. We also present a set of directions for future work for an increasingly representative classification of evolution in science, which is not intended as airtight, but flexible and perhaps increasingly based on phenomena and not just disciplines.A evolução na produção de ciência, associada à crescente colaboração interdomínios do conhecimento e à também crescente coautoria de trabalhos permanece suportada por processos de classificação manual, subjetiva e sujeita a interpretações erradas. A própria taxonomia na qual assenta esse mesmo processo de classificação não é consensual, com organismos estatais a recorrerem a taxonomias que não acompanham as alterações nas áreas científicas, e indexadores/repositórios que procuram acompanhar essas mesmas alterações. Verificamos uma realidade distinta do espectável e que os domínios onde são registados os trabalhos científicos podem facilmente estar desenquadrados. A taxonomia hoje aplicada pelos organismos governamentais, como o caso do organismo que regulamenta a produção científica em Portugal, não é suficiente, é limitadora, e promove a classificação em domínios aproximados do desejado, logo com grande potencial para erro. Um processo de classificação automática com base em algoritmos de machine learning apresenta-se como uma possível solução para o problema da subjetividade na classificação, e embora não resolva a questão do desenquadramento da taxonomia utilizada, é apresentada neste trabalho como uma possibilidade comprovada. Neste trabalho propomos uma taxonomia de classificação, bem como nós desenvolvemos um processo baseado em machine learning algoritmos para resolver o problema de classificação. Apresentamos ainda um conjunto de direções para trabalhos futuros para uma classificação cada vez mais representativa da evolução nas ciências, que não pretende ser hermética, mas flexível e talvez cada vez mais baseada em fenómenos e não apenas em disciplinas

Spatially Integrated Social Sciences with Qualitative GIS to Support Impact Assessment in Mining Communities

Spatially integrated social science is a broad term used to describe the integration of space and place in social science research using Geographic Information Systems (GIS). It includes qualitative GIS approaches, such as geo-ethnology and geo-narratives, which combine qualitative social data with GIS and represent an emerging approach with significant potential for facilitating new insights into the dynamic interactions between mining companies and host communities. Mine operations are unique in their complexity, both in terms of the dynamic and diverse nature of issues and the requirement to integrate knowledge, theories, and approaches from a range of disciplines. In this paper we describe the potential for spatially integrated social science using qualitative GIS to understand the social impacts of mining. We review current literature and propose a framework that incorporates quantitative and qualitative knowledge across social and biophysical domains within a multi-user approach. We provide examples to illustrate how our approach could support past, present, and future assessment of socio-environmental systems in large-scale mining. We conclude by discussing the need for a multi-disciplinary approach to support decision makers and local stakeholders in considering complex social and environmental scenario

Big Data for Public Domain: A bibliometric and visualized study of the scientific discourse during 2000–2020

This article aims to investigate the trend of scientific publications under ‘big data and policy’ research during the last two decades, including the dynamics of the network structure of researchers and the institutions. Bibliometrics is utilized as a tool to reveal the dynamics of scientific discussions that occur through articles, published in international journals indexed/contained in the Scopus database; meanwhile, the analysis visualization is performed by using VOSviewer 1.6.16. The search results indicate that the United States serves as the country of origin for most productive author affiliations in publishing articles, the University of Oxford (United Kingdom) serves as the home institution for most productive author affiliations, and Williamson, B., from the University of Edinburgh (United Kingdom), is considered as the most prolific writer. In addition, the Swiss Sustainability Journal from MDPI is cited as the source for the most widely discussed publication topic in its journals. Further, ‘Big Data for Development: A Review of Promises and Challenges’ is regarded as the article with the most references. Additionally, the most discussed topics on ‘big data and policy’ include smart cities, open data, privacy, artificial intelligence, machine learning, and data science

The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations

In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps