Maps of science as interdisciplinary discourse: co-citation contexts and the role of analogy

Machine learning misclassification networks reveal a citation advantage of interdisciplinary publications only in high-impact journals

Given a large enough volume of data and precise, meaningful categories, training a statistical model to solve a classification problem is straightforward and has become a standard application of machine learning (ML). If the categories are not precise, but rather fuzzy, as in the case of scientific disciplines, the systematic failures of ML classification can be informative about properties of the underlying categories. Here we classify a large volume of academic publications using only the abstract as information. From the publications that are classified differently by journal categories and ML categories (i.e., misclassified publications, when using the journal assignment as ground truth) we construct a network among disciplines. Analysis of these misclassifications provides insight in two topics at the core of the science of science: (1) Mapping out the interplay of disciplines. We show that this misclassification network is informative about the interplay of academic disciplines and it is similar to, but distinct from, a citation-based map of science, where nodes are scientific disciplines and an edge indicates a strong co-citation count between publications in these disciplines. (2) Analyzing the success of interdisciplinarity. By evaluating the citation patterns of publications, we show that misclassification can be linked to interdisciplinarity and, furthermore, that misclassified articles have different citation frequencies than correctly classified articles: In the highest 10 percent of journals in each discipline, these misclassified articles are on average cited more frequently, while in the rest of the journals they are cited less frequently.

Blockchain technology-based sustainable management research: the status quo and a general framework for future application

The problems of data leakage and unreliable information transfer in the management process make sustainability management an inevitable need for future development. Globally, there is increasing attention paid to blockchain technology and particularly its application in addressing sustainable management issues, both from academia and industry. Aiming to deepen the understanding of how blockchain technology could deal with sustainable management issues across different disciplines, this paper investigates the latest research on the application of blockchain technology in sustainable management published from 2017 to 2021. It is found that there is a drastic surge of publications in the recent 2 years. The analysis focuses on authors' origins, the collaboration network of the keywords, countries, and research topics covered. The application of blockchain technology in five key sectors of sustainable management, encompassing energy management, construction management, supply chain management, environmental management, and e-government management, is selected for further analysis detail. Also, a general framework for applying blockchain technology is proposed for broadening its use and dealing with sustainable management issues. The findings show that the identified 108 publications are distributed in 75 different journals, and scholars from China, the UK, and the USA have been working closely in BT-based sustainable management research. Blockchain technology is just emerging in sustainable management, and there is a great potential for applying blockchain technology to improve sustainable management performance and, more importantly, to provide solutions to quite a few long-lasting problems in these sectors. Opportunities for future research are also presented and discussed.

Research disciplinary interactions on scientific collaboration network in photocatalytic hydrogen evolution: Characteristics and dynamics

Interdisciplinary scientific collaboration promotes the innovative development of scientific research. Photocatalytic hydrogen evolution (PHE) is a typical interdisciplinary subject. This study aims to explore the characteristics of discipline interaction and the temporal evolution in the field. Bibliometric analysis could be used to understand the stage of research in a particular subject. In this work, the publications on the topic in Web of Science (WoS) platform from 1999 to 2020 were selected. On the basis of social network theory, the characteristics of interdisciplinary were revealed from three perspectives. First, the disciplinary interaction network is constructed through disciplinary co-occurrence to detect the characteristics of interaction structure among different disciplines. Then the node centrality index is employed to explore the influence of disciplines in the interactive network by using network centrality analysis. Moreover, the dynamic of discipline interaction evolution is studied using blockmodeling analysis. In the field of PHE, the number of disciplines and the intensity of interaction among different subjects gradually increased in the past 20 years. Chemistry and Material Sciences are the core discipline, and they play an important role in the network. The whole network is divided into different discipline groups. The scale of the discipline group is becoming large, and the disciplinary interaction is becoming more complex. The obtained results are helpful for guiding scholars to carry out interdisciplinary interaction. The methods of detecting interdisciplinary interactive relationship could provide paths for interdisciplinary research in other fields.

A bibliometric analysis of comparative research on the evolution of international and Chinese green supply chain research hotspots and frontiers

Green supply chain (GSC), one of the most vital sub-topics of sustainable development, indicates people provoking on the rationality of business practices and resource consumption patterns. Under the background of economy globalization, developing countries, especially China, severely affected by green barriers became the global focus. A systematic review of articles about GSC which published in leading journals of Web of Science Core Collection (WoSCC) and China Knowledge Resource Infrastructure (CNKI) is proposed for exploring publishing trends, the distribution of authors and journals, research topics, and hotspots and predicting frontiers by utilizing VOSviewer, Sci2, and CiteSpace. The results show that (1) there are differences in the attention of GSC between international and Chinese academia. (2) "Green" is referred to environmental friendly practices in international academia. Scholars advocate to promote management to strengthen cooperation among GSC members and boost technology investment to improve the comprehensive performance; however, specific practices such as "low-carbon," "emission reduction," "recycling," and "remanufacture" are referred to environmental friendly behaviors in Chinese academia. Scholars expect to avoid enterprises' short-term profit compression relying on government subsidies and make contracts to share environment protection cost equally out for ensuring GSC stable operation. (3) Exploring collaboration among GSC members using complex operation research and artificial intelligence will be international research frontier. Relevant papers are to provide Chinese research with merely innovation in methodology. Besides, the "government-enterprise-university-research institute-customer-economy" management mode proposed by development countries like China will enrich the international GSC research scope, leading international GSC knowledge structure to change. The contribution of this study is to afford reference for future research on GSC.

Machine learning misclassification of academic publications reveals non-trivial interdependencies of scientific disciplines

Exploring the production of knowledge with quantitative methods is the foundation of scientometrics. In an application of machine learning to scientometrics, we here consider the classification problem of the mapping of academic publications to the subcategories of a multidisciplinary journal—and hence to scientific disciplines—based on the information contained in the abstract. In contrast to standard classification tasks, we are not interested in maximizing the accuracy, but rather we ask, whether the failures of an automatic classification are systematic and contain information about the system under investigation. These failures can be represented as a ’misclassification network’ inter-relating scientific disciplines. Here we show that this misclassification network (1) gives a markedly different pattern of interdependencies among scientific disciplines than common ’maps of science’, (2) reveals a statistical association between misclassification and citation frequencies, and (3) allows disciplines to be classified as ’method lenders’ and ’content explorers’, based on their in-degree out-degree asymmetry. On a more general level, in a wide range of machine learning applications misclassification networks have the potential of extracting systemic information from the failed classifications, thus allowing to visualize and quantitatively assess those aspects of a complex system, which are not machine learnable.

Tracking and Mining the COVID-19 Research Literature

The unprecedented, explosive growth of the COVID-19 domain presents challenges to researchers to keep up with research knowledge within the domain. This article profiles this research to help make that knowledge more accessible via overviews and novel categorizations. We provide websites offering means for researchers to probe more deeply to address specific questions. We further probe and reassemble COVID-19 topical content to address research issues concerning topical evolution and emphases on tactical vs. strategic approaches to mitigate this pandemic and reduce future viral threats. Data suggest that heightened attention to strategic, immunological factors is warranted. Connecting with and transferring in research knowledge from outside the COVID-19 domain demand a viable COVID-19 knowledge model. This study provides complementary topical categorizations to facilitate such modeling to inform future Literature-Based Discovery endeavors.

Detecting the Interdisciplinary Nature and Topic Hotspots of Robotics in Surgery: Social Network Analysis and Bibliometric Study

Background

With the widespread application of a robot to surgery, growing literature related to robotics in surgery (RS) documents widespread concerns from scientific researchers worldwide. Although such application is helpful to considerably improve the accuracy of surgery, we still lack the understanding of the multidiscipline-crossing status and topic distribution related to RS.

Objective

The aim of this study was to detect the interdisciplinary nature and topic hotspots on RS by analyzing the current publication outputs related to RS.

Methods

The authors collected publications related to RS in the last 21 years, indexed by the Web of Science Core Collection. Various bibliometric methods and tools were used, including literature distribution analysis at the country and institution level and interdisciplinary collaboration analysis in the different periods of time. Co-word analysis was performed based on the keywords with high frequency. The temporal visualization bar presented the evolution of topics over time.

Results

A total of 7732 bibliographic records related to RS were identified. The United States plays a leading role in the publication output related to RS, followed by Italy and Germany. It should be noted that the Yonsei University in South Korea published the highest number of RS-related publications. Furthermore, the interdisciplinary collaboration is uneven; the number of disciplines involved in each paper dropped from the initial 1.60 to the current 1.31. Surgery; Engineering; Radiology, Nuclear Medicine, and Medical Imaging; and Neurosciences and Neurology are the 4 core disciplines in the field of RS, all of which have extensive cooperation with other disciplines. The distribution of topic hotspots is in imbalanced status, which can be categorized into 7 clusters. Moreover, 3 areas about the evolution of topic were identified, namely (1) the exploration of techniques that make RS implemented, (2) rapid development of robotic systems and related applications in surgery, and (3) application of a robot to excision of tissues or organs targeted at various specific diseases.

Conclusions

This study provided important insights into the interdisciplinary nature related to RS, which indicates that the researchers with different disciplinary backgrounds should strengthen cooperation to publish a high-quality output. The research topic hotspots related to RS are relatively scattered, which has begun to turn to the application of RS targeted at specific diseases. Our study is helpful to provide a potential guide to the direction of the field of RS for future research in the field of RS.

Emerging approaches in literature-based discovery: techniques and performance review

Literature-based discovery systems aim at discovering valuable latent connections between previously disparate research areas. This is achieved by analyzing the contents of their respective literatures with the help of various intelligent computational techniques. In this paper, we review the progress of literature-based discovery research, focusing on understanding their technical features and evaluating their performance. The present literature-based discovery techniques can be divided into two general approaches: the traditional approach and the emerging approach. The traditional approach, which dominate the current research landscape, comprises mainly of techniques that rely on utilizing lexical statistics, knowledge-based and visualization methods in order to address literature-based discovery problems. On the other hand, we have also observed the births of new trends and unprecedented paradigm shifts among the recently emerging literature-based discovery approach. These trends are likely to shape the future trajectory of the next generation literature-based discovery systems.

Mapping interdisciplinary fields: efficiencies, gaps and redundancies in HIV/AIDS research

While interdisciplinarity continues to increase in popularity among funders and other scientific organizations, its potential to promote scientific advances remains under-examined. For HIV/AIDS research, we examine the dynamics of disciplinary integration (or lack thereof) providing insight into a field's knowledge base and those questions that remain unresolved. Drawing on the complete histories of two interdisciplinary journals, we construct bibliographic coupling networks based on overlapping citations to identify segregation into research clusters and estimate topic models of research content. We then compare how readily those bibliographic coupling clusters account for the structuring of topics covered within the field as it evolves over two decades. These comparisons challenge one-dimensional and/or cross-sectional approaches to interdisciplinarity. Some topics are increasingly coordinated across disciplinary boundaries (e.g., vaccine development); others remain relatively segmented into disconnected disciplinary domains for the full period (e.g., drug resistance). This divergence indicates heterogeneity in interdisciplinarity and emphasizes the need for critical approaches to studying the organization of science.

Entitymetrics: measuring the impact of entities

This paper proposes entitymetrics to measure the impact of knowledge units. Entitymetrics highlight the importance of entities embedded in scientific literature for further knowledge discovery. In this paper, we use Metformin, a drug for diabetes, as an example to form an entity-entity citation network based on literature related to Metformin. We then calculate the network features and compare the centrality ranks of biological entities with results from Comparative Toxicogenomics Database (CTD). The comparison demonstrates the usefulness of entitymetrics to detect most of the outstanding interactions manually curated in CTD.