Is entrepreneurship an emerging area of research? A computational response

Objective: We aim to answer four questions. First, with the increasing number of publications, is there a concentration in specific subjects, or on the contrary, a dispersion, amplifying the span of themes related to entrepreneurship? Second, is there a hierarchy of subjects, in the sense that some of them constitute the “core” of entrepreneurship? Third, are they connected with other established research areas? Finally, it is possible to identify papers that are influential, acting as hubs in the cluster’s formation? Method: We developed an original version of the computational procedure proposed by Shibata et al (2008), which allows us to understand the diversity of the different sub-areas of the topic investigated, reducing the need for specialist supervision. Originality / Relevance: We developed and applied a method to capture the formation and evolution of research areas in entrepreneurship literature, via direct citation networks, allowing us to understand the iteration between the different research sub-areas. Results: The dispersion is a feature of entrepreneurship as field research, with a hierarchy between research areas, indicating an emergent organization in the expansion processes. We concluded that research on entrepreneurship consists of specialization, that is, by application in niches.

The Motif «My Strength is Made Perfect in Weakness» in N. V. Gogol’s Epistolary Heritage of the 1840s

Статья посвящена исследованию библейского мотива «сила Моя в немощи совершается» в письмах Н. В. Гоголя 1840х гг. Источником данного мотива является фрагмент Второго послания апостола Павла к Коринфянам. Определяются особенности авторской интерпретации апостольского послания, выявляются основные способы его включения в эпистолярий Гоголя (прямое цитирование, аллюзия). The article is devoted to the study of the biblical motive «My strength is made perfect in weakness» in the letters of N. V. Gogol in the 1840s. The source of this motive is a fragment of the second epistle of the Apostle Paul to the Corinthians. The author defines the peculiarities of the author’s interpretation of the apostolic epistle, identifies the main ways of its inclusion in Gogol’s epistolary (direct citation, allusion).

Energy Efficiency in Cloud Computing: Exploring the Intellectual Structure of the Research Field and Its Research Fronts with Direct Citation Analysis

The aim of the study is to explore the intellectual structure of the field and fronts in research on energy efficiency in the context of cloud computing and thus to contribute to science mapping of the research field. The research process was driven by the following study questions: (1) what are the most influential publications in the research field? and (2) what are the research fronts in the research field? The method of direct citation analysis was employed in the research process. Data for analysis were obtained from the Scopus database and analyzed with the use of VOSviewer science mapping software. In response to the first question, we identified the most influential publications in the research field and analyzed their types (i.e., whether they are original research papers or rather the “context” papers e.g., survey or review papers, framework papers, challenges papers, and study papers). Moreover, a comparison analysis between the types of papers among the most cited “classical” publications and “emerging stars” was conducted. In response to the second research question, we identified five research fronts concentrated around the issues of: virtual machine management (“VM”); task-focus, concerning data replication, task consolidation, and task scheduling (“task”); energy efficiency (“energy”); modelling and optimization (“model”); and energy efficiency in the networking context (“network”).

Mapping the biomedical sciences using Medical Subject Headings: a comparison between MeSH co-assignments and MeSH citation pairs

Objective: This study compares two maps of biomedical sciences using Medical Subject Headings (MeSH) term co-assignments versus MeSH terms of citing/cited articles and reveals similarities and differences between the two approaches. Methods: MeSH terms assigned to 397,475 journal articles published in 2015, as well as their 4,632,992 cited references, were retrieved from Web of Science and MEDLINE databases, respectively, which formed over 7 million MeSH co-assignments and nearly 18 million direct citation pairs. We generated six network visualizations of biomedical science at three levels using Gephi software based on these MeSH co-assignments and citation pairs.Results: The MeSH co-assignment map contained more nodes and edges, as MeSH co-assignments cover all medical topics discussed in articles. By contrast, the MeSH citation map contained fewer but larger nodes and wider edges, as citation links indicate connections to two similar medical topics. Conclusion: These two types of maps emphasize different aspects of biomedical sciences, with MeSH co-assignment maps focusing on the relationship between topics in different categories and MeSH direct citation maps providing insights into relationships between topics in the same or similar category.

Article-level classification of scientific publications: A comparison of deep learning, direct citation and bibliographic coupling

Classification schemes for scientific activity and publications underpin a large swath of research evaluation practices at the organizational, governmental, and national levels. Several research classifications are currently in use, and they require continuous work as new classification techniques becomes available and as new research topics emerge. Convolutional neural networks, a subset of “deep learning” approaches, have recently offered novel and highly performant methods for classifying voluminous corpora of text. This article benchmarks a deep learning classification technique on more than 40 million scientific articles and on tens of thousands of scholarly journals. The comparison is performed against bibliographic coupling-, direct citation-, and manual-based classifications—the established and most widely used approaches in the field of bibliometrics, and by extension, in many science and innovation policy activities such as grant competition management. The results reveal that the performance of this first iteration of a deep learning approach is equivalent to the graph-based bibliometric approaches. All methods presented are also on par with manual classification. Somewhat surprisingly, no machine learning approaches were found to clearly outperform the simple label propagation approach that is direct citation. In conclusion, deep learning is promising because it performed just as well as the other approaches but has more flexibility to be further improved. For example, a deep neural network incorporating information from the citation network is likely to hold the key to an even better classification algorithm.

The Bible in Modern Chinese Fiction

This essay surveys existing scholarship on the appearance and use of the Bible in modern Chinese fiction, including chronological, biographical, and thematic studies, while offering its own approach to the Bible in fiction through the type and degree of literary engagement. This ranges from direct, sustained dialogue with the Bible, as in stories based on a particular biblical scene or pericope or the many semi-fictional Lives of Christ produced in the first half of the twentieth century, through to much more diffuse or passing references to biblical themes or allusions. The essay begins with the representation of the physical Bible in literature before considering the transcribed Bible, in direct citation of Bible passages in stories or novels, while the second part of the essay considers literary or thematic engagement.

Introducing Patents with Indirect Connection (PIC) for Establishing Patent Strategies

A patent system requires novelty and progressiveness so that new patents do not infringe on the rights of prior art. Patent investigation including a prior art search is essential to the process of commercialization of technology. In general, patent investigation has been conducted by experts based on their qualitative judgement. However, the number of patents has increased so fast that it has become difficult to handle the quantitative burdens of the search with a conventional approach. There have been previous studies dealing with patent investigation to find similar technologies. They had limitations as they did not utilize the citation relationship and similarity between patents in a comprehensive way. In addition, they could not properly reflect the sequential citation relationship of patents though this is effective in discovering similar patents. In this study, we propose an efficient methodology to discover similar technologies by comprehensively considering the similarity and citation relationship between patents. In particular, we intended to reflect the citation sequence and indirect citation relationship in the process of searching for similar patents. For this, we introduced the concept of “patents with indirect connections” (PICs) and devised an algorithm to efficiently detect patent pairs having such a relationship. The proposed methodology of this study contributes to preventing patent litigation in advance by discovering patents with such potential risks. It is expected that this method will provide patent applicants with the opportunity to establish appropriate strategies against competitors with similar technologies. In order to examine the practical applicability of the proposed method, Korean patents related to machine learning and deep learning were collected. As a result of the experiment, it was possible to identify 24 pairs of similar patents without a direct citation relationship and derive appropriate counter strategies.

Intertextuality in the novella Vasylko Rostyslavych by Volodymyr Birchak

The article dwells on the personality of Volodymyr Birchak, outlining his activities, examining his creative heritage, and giving a brief overview of the studies of V. Birchak’s historical novellas. Emphasizing the polemics about the methods of artistic comprehension and interpretation of the events of Kievan Rus by V. Birchak, the author points out that intertextuality in the novella Vasilko Rostislavich has not become the subject of research by literary scholars yet. However, intertextuality is extremely indicative for V. Birchak’s novella under analysis, since it characterizes the writer as an expert on the monuments of Old Russian literature and demonstrates his style. The author determines different ways of referring to other texts in the novella: citing the original source, mentioning the title of a known text, combining the translation with direct citation of a fragment. Analyzing the situations in which other texts appear in Vasilko Rostislavich, the author notes that the most frequently cited texts are The Tale of Bygone Years, The Tale of Igor’s Campaign, Rus’ Justice, Pchela. Emphasizing the motivation for referring to the iconic texts of Ukrainian culture in the writer’s work, the author reveals the functions of such narratives in the novella under study. Intertextual connections and their dialogical relations are manifested both at the level of content and form. Further research can focus on intertextuality in other works about Kievan Rus.

A comparison of large-scale science models based on textual, direct citation and hybrid relatedness

Recent large-scale bibliometric models have largely been based on direct citation, and several recent studies have explored augmenting direct citation with other citation-based or textual characteristics. In this study we compare clustering results from direct citation, extended direct citation, a textual relatedness measure, and several citation-text hybrid measures using a set of nine million documents. Three different accuracy measures are employed, one based on references in authoritative documents, one using textual relatedness, and the last using document pairs linked by grants. We find that a hybrid relatedness measure based equally on direct citation and PubMed-related article scores gives more accurate clusters (in the aggregate) than the other relatedness measures tested. We also show that the differences in cluster contents between the different models are even larger than the differences in accuracy, suggesting that the textual and citation logics are complementary. Finally, we show that for the hybrid measure based on direct citation and related article scores, the larger clusters are more oriented toward textual relatedness, while the smaller clusters are more oriented toward citation-based relatedness.