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Classifying papers according to the fields of knowledge is critical to clearly understand the dynamics of scientific (sub)fields, their leading questions, and trends. Most studies rely on journal categories defined by popular databases such as WoS or Scopus, but some experts find that those categories may not correctly map the existing subfields nor identify the subfield of a specific article. This study addresses the classification problem using data from each paper (Abstract, Title, Keywords, and the KeyWords Plus) and the help of experts to identify the existing subfields and journals exclusive of each subfield. These "exclusive journals" are critical to obtain, through a pattern detection procedure that uses machine learning techniques (from software NVivo), a list of the frequent terms that are specific to each subfield. With that list of terms and with the help of optimization procedures, we can identify to which subfield each paper most likely belongs. This study can contribute to support scientific policy‐makers, funding, and research institutions—via more accurate academic performance evaluations—, to support editors in their tasks to redefine the scopes of journals, and to support popular databases in their processes of refining categories. [ABSTRACT FROM AUTHOR]

Purpose: To analyze the AI research in the field of nursing, to explore the current situation, hot topics, and prospects of AI research in the field of nursing, and to provide a reference for researchers to carry out related studies. Methods: We used the VOSviewer 1.6.17, SciMAT, and CiteSpace 5.8.R3 to generate visual cooperation network maps for the country, organizations, authors, citations, and keywords and perform burst detection, theme evolution, and so forth. Findings A total of 9318 articles were obtained from the Web of Science Core Collection database. Four hundred and thirty‐one AI research related to the field of nursing was published by 855 institutions from 54 countries. CIN‐Computers Informatics Nursing was the top productive journal. The United States was the dominant country. The transnational cooperation between authors from developed countries was closer than that between authors from developing countries. The main hot topics included nurse rostering, nursing diagnosis, nursing decision support, disease risk factor prediction, nursing big data management, expert system, support vector machine, decision tree, deep learning, natural language processing, and nursing education. Machine learning represented one of the cutting‐edge and most applicable branches of artificial intelligence in the field of nursing, and deep learning was the hottest technology among many machine learning methods in recent years. One of the most cited papers was published by Burke in 2004 and cited 500 times, which critically evaluated AI methods to deal with nurse scheduling problems. Conclusions: Although AI has been paid more and more attention to the field of nursing, there is still a lack of high‐yielding authors who have been engaged in this field for a long time. Most of the high contribution authors and institutions came from developed countries; therefore, more transnational and multi‐disciplinary cooperation is needed to promote the development of AI in the nursing field. This bibliometric analysis not only provided a comprehensive overview to help researchers to understand the important articles, journals, potential collaborators, and institutions in this field but also analyzed the history, hot spots, and future trends of the research topic to provide inspiration for researchers to choose research directions. [ABSTRACT FROM AUTHOR]

Research activities related to social informatics (SI) are expanding, even as community fragmentation, topical dispersion, and methodological diversity continue to increase. Specifically, the different understandings of SI in regional communities have strong impacts, and each has a different history, methodological grounding, and often a different thematic focus. The aim of this article is to connect three selected perspectives on SI—intellectual (regional schools of thought), methodological, and thematic—and introduce a comparative framework for understanding SI that includes all known approaches. Thus, the article draws from a thematic and methodological grounding of research across schools of thought, along with definitions that rely on the extension and intension of the notion of SI. The article is built on a paralogy of views and pluralism typical of postmodern science. Because SI is forced to continually reform its research focus, due to the rapid development of information and communication technology, social changes and ideologies that surround computerization and informatization, the presented perspective maintains a high degree of flexibility, without the need to constantly redefine the boundaries, as is typical in modern science. This approach may support further developments in promoting and understanding SI worldwide. [ABSTRACT FROM AUTHOR]

Using information theory, we measure innovation systemness as synergy among size‐classes, ZIP Codes, and technological classes (NACE‐codes) for 8.5 million American companies. The synergy at the national level is decomposed at the level of states, Core‐Based Statistical Areas (CBSA), and Combined Statistical Areas (CSA). We zoom in to the state of California and in more detail to Silicon Valley. Our results do not support the assumption of a national system of innovations in the U.S.A. Innovation systems appear to operate at the level of the states; the CBSA are too small, so that systemness spills across their borders. Decomposition of the sample in terms of high‐tech manufacturing (HTM), medium‐high‐tech manufacturing (MHTM), knowledge‐intensive services (KIS), and high‐tech services (HTKIS) does not change this pattern, but refines it. The East Coast—New Jersey, Boston, and New York—and California are the major players, with Texas a third one in the case of HTKIS. Chicago and industrial centers in the Midwest also contribute synergy. Within California, Los Angeles contributes synergy in the sectors of manufacturing, the San Francisco area in KIS. KIS in Silicon Valley and the Bay Area—a CSA composed of seven CBSA—spill over to other regions and even globally. [ABSTRACT FROM AUTHOR]

In this article we use a stated choice experiment to study researcher preferences in the information sciences and to investigate the relative importance of different journal characteristics in convincing potential authors to submit to a particular journal. The analysis distinguishes high quality from standard quality articles and focuses on the question whether communicating acceptance rates rather than rejection rates leads to other submission decisions. Our results show that a positive framing effect might be present when authors decide on submitting a high quality article. No evidence of a framing effect is found when authors consider a standard quality article. From a journal marketing perspective, this is important information for editors. Communicating acceptance rates rather than rejection rates might help to convince researchers to submit to their journal. [ABSTRACT FROM AUTHOR]