Search (172 results, page 3 of 9)

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Abstract

We use a technique recently developed by V. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre (2008) to detect scientific specialties from author cocitation networks. This algorithm has distinct advantages over most previous methods used to obtain cocitation clusters since it avoids the use of similarity measures, relies entirely on the topology of the weighted network, and can be applied to relatively large networks. Most importantly, it requires no subjective interpretation of the cocitation data or of the communities found. Using two examples, we show that the resulting specialties are the smallest coherent groups of researchers (within a hierarchy of cluster sizes) and can thus be identified unambiguously. Furthermore, we confirm that these communities are indeed representative of what we know about the structure of a given scientific discipline and that as specialties, they can be accurately characterized by a few keywords (from the publication titles). We argue that this robust and efficient algorithm is particularly well-suited to cocitation networks and that the results generated can be of great use to researchers studying various facets of the structure and evolution of science.

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Abstract

The rapid advancement of nanotechnology research and development during the past decade presents an excellent opportunity for a scientometric study because it can provide insights into the dynamic growth of the fast-evolving social networks associated with this field. In this article, we describe a case study conducted on nanotechnology to discover the dynamics that govern the growth process of rapidly advancing scientific-collaboration networks. This article starts with the definition of temporal social networks and demonstrates that the nanotechnology collaboration network, similar to other real-world social networks, exhibits a set of intriguing static and dynamic topological properties. Inspired by the observations that in collaboration networks new connections tend to be augmented between nodes in proximity, we explore the locality elements and the attachedness factor in growing networks. In particular, we develop two distance-based computational network growth schemes, namely the distance-based growth model (DG) and the hybrid degree and distance-based growth model (DDG). The DG model considers only locality element while the DDG is a hybrid model that factors into both locality and attachedness elements. The simulation results from these models indicate that both clustering coefficient rates and the average shortest distance are closely related to the edge densification rates. In addition, the hybrid DDG model exhibits higher clustering coefficient values and decreasing average shortest distance when the edge densification rate is fixed, which implies that combining locality and attachedness can better characterize the growing process of the nanotechnology community. Based on the simulation results, we conclude that social network evolution is related to both attachedness and locality factors.

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Abstract

Previous analyses identified research on environmental tobacco smoke to be subject to strong fluctuations as measured by both quantitative and qualitative indicators. The evolution of search algorithms (based on the Web of Science and Web of Knowledge database platforms) was used to show the impact of errors of omission and commission in the outcomes of scientometric research. Optimization of the search algorithm led to the complete reassessment of previously published findings on the performance of environmental tobacco smoke research. Instead of strong continuous growth, the field of environmental tobacco smoke research was shown to experience stagnation or slow growth since mid-1990s when evaluated quantitatively. Qualitative analysis revealed steady but slow increase in the citation rate and decrease in uncitedness. Country analysis revealed the North-European countries as leaders in environmental tobacco smoke research (when the normalized results were evaluated both quantitatively and qualitatively), whereas the United States ranked first only when assessing the total number of papers produced. Scientometric research artifacts, including both errors of omission and commission, were shown to be capable of completely obscuring the real output of the chosen research field.

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Abstract

This article analyzes the context of citations within the full text of research articles. It studies articles published in a single journal: the Journal of Informetrics (JOI), in the first year the journal was published, 2007. The analysis classified the citations into in- and out-disciplinary content and looked at their use within the articles' sections such as introduction, literature review, methodology, findings, discussion, and conclusions. In addition, it took into account the age of cited articles. A thematic analysis of these citations was performed in order to identify the evolution of topics within the articles sections and the journal's content. A matrix describing the relationships between the citations' use, and their in- and out-disciplinary focus within the articles' sections is presented. The findings show that an analysis of citations based on their in- and out-disciplinary orientation within the context of the articles' sections can be an indication of the manner by which cross-disciplinary science works, and reveals the connections between the issues, methods, analysis, and conclusions coming from different research disciplines.

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Abstract

Whereas traditional science maps emphasize citation statistics and static relationships, this paper presents a term-based method to identify and visualize the evolutionary pathways of scientific topics in a series of time slices. First, we create a data preprocessing model for accurate term cleaning, consolidating, and clustering. Then we construct a simulated data streaming function and introduce a learning process to train a relationship identification function to adapt to changing environments in real time, where relationships of topic evolution, fusion, death, and novelty are identified. The main result of the method is a map of scientific evolutionary pathways. The visual routines provide a way to indicate the interactions among scientific subjects and a version in a series of time slices helps further illustrate such evolutionary pathways in detail. The detailed outline offers sufficient statistical information to delve into scientific topics and routines and then helps address meaningful insights with the assistance of expert knowledge. This empirical study focuses on scientific proposals granted by the United States National Science Foundation, and demonstrates the feasibility and reliability. Our method could be widely applied to a range of science, technology, and innovation policy research, and offer insight into the evolutionary pathways of scientific activities.

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Abstract

Benford's law is an empirical observation, first reported by Simon Newcomb in 1881 and then independently by Frank Benford in 1938: the first significant digits of numbers in large data are often distributed according to a logarithmically decreasing function. Being contrary to intuition, the law was forgotten as a mere curious observation. However, in the last two decades relevant literature has grown exponentially-an evolution typical of "Sleeping Beauties" (SBs) publications that go unnoticed (sleep) for a long time and then suddenly become the center of attention (are awakened). Thus, in the present study, we show that the two papers, Newcomb (1881) and Benford (1938), Newcomb (1881, American Journal of Mathematics, 4, 39-40) and Benford (1938, Proc. Am. Phil. Soc., 78, 551-572) papers are clearly SBs. The former was in a deep sleep for 110 years, whereas the latter was in a deep sleep for a comparatively lesser period of 31 years up to 1968, and in a state of less deep sleep for another 27 years, up to 1995. Both SBs were awakened in the year 1995 by Hill (1995a, Statistical Science, 10, 354-363). In so doing, we show that the waking prince (Hill, 1995a) is more often quoted than the SB whom he kissed-in this Benford's law case, wondering whether this is a general effect-to be usefully studied.

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Abstract

Annual journal rankings are usually considered a tool for the evaluation of research and researchers. Although they are an objective resource for such evaluation, they also present drawbacks: (a) the uncertainty about the definite position of a target journal in the corresponding annual ranking when selecting a journal, and (b) in spite of the nonsignificant difference in score (for instance, impact factor) between consecutive journals in the ranking, the journals are strictly ranked and eventually placed in different terciles/quartiles, which may have a significant influence in the subsequent evaluation. In this article we present several proposals to obtain an aggregated consensus ranking as an alternative/complementary tool to standardize annual rankings. To illustrate the proposed methodology we use as a case study the Journal Citation Reports, and in particular the category of Computer Science: Artificial Intelligence (CS:AI). In the context of the consensus rankings obtained by the different methods, we discuss the convenience of using one or the other procedure according to the corresponding framework. In particular, our proposals allow us to obtain consensus rankings that avoid crisp frontiers between similarly ranked journals and consider the longitudinal/temporal evolution of the journals.

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Abstract

Analysis of the knowledge structure and the evolution of research topics in specific areas has been the focus of information science (IS). Such analysis helps to enrich interested researchers' understanding of the functions, activities and evolving constraints of the knowledge domain. This study aims to investigate the knowledge domain associated with the vulnerability assessment in the context of climate change (VACC) research in this fast-growing field between January 1991 and December 2017. A bibliometric approach, along with CiteSpace software, was used to identify and visualize thematic patterns, landmark articles and emerging trends. The data used for the bibliometric analysis include 6,584 original research articles and reviews published between 1991 and 2017. The results indicate that the number of documents pertaining to VACC presented a general growth trend over the last twenty-seven years. Climatic Change was the most productive journal. Among countries, the USA, England and Australia predominated, and the University of Chinese Academy of Sciences and U.S. Geological Survey were the two institutions with the largest amount of VACC research. Existing studies in the field of VACC research have focused primarily on environmental sciences. Importantly, emerging trends in VACC research have shifted away from vulnerability assessments of natural ecosystems based on model simulation methods in the context of climate change toward indicator-based assessments of social ecosystem vulnerability, adaptive capacity and resilience under multidimensional stressors and shocks, which are likely to define the new frontier in the field of VACC research.

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Abstract

Main path analysis is a famous network-based method for understanding the evolution of a scientific domain. Most existing methods have two steps, weighting citation arcs based on search path counting and exploring main paths in a greedy fashion, with the assumption that citation networks are acyclic. The only available proposal that avoids manual cycle removal is to preprint transform a cyclic network to an acyclic counterpart. Through a detailed discussion about the issues concerning this approach, especially deriving the "de-preprinted" main paths for the original network, this article proposes an alternative solution with two-fold contributions. Based on the argument that a publication cannot influence itself through a citation cycle, the SimSPC algorithm is proposed to weight citation arcs by counting simple search paths. A set of algorithms are further proposed for main path exploration and extraction directly from cyclic networks based on a novel data structure main path tree. The experiments on two cyclic citation networks demonstrate the usefulness of the alternative solution. In the meanwhile, experiments show that publications in strongly connected components may sit on the turning points of main path networks, which signifies the necessity of a systematic way of dealing with citation cycles.

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Abstract

Efforts to combat continuing gender inequalities in academia need to be informed by evidence about where differences occur. Citations are relevant as potential evidence in appointment and promotion decisions, but it is unclear whether there have been historical gender differences in average citation impact that might explain the current shortfall of senior female academics. This study investigates the evolution of gender differences in citation impact 1996-2018 for six million articles from seven large English-speaking nations: Australia, Canada, Ireland, Jamaica, New Zealand, UK, and the USA. The results show that a small female citation advantage has been the norm over time for all these countries except the USA, where there has been no practical difference. The female citation advantage is largest, and statistically significant in most years, for Australia and the UK. This suggests that any academic bias against citing female-authored research cannot explain current employment inequalities. Nevertheless, comparisons using recent citation data, or avoiding it altogether, during appointments or promotion may disadvantage females in some countries by underestimating the likely greater impact of their work, especially in the long term.

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Abstract

Interdisciplinary research is widely recognized as necessary to tackle some of the grand challenges facing humanity. It is generally believed that interdisciplinarity is becoming increasingly prevalent among Science, Technology, Engineering, and Mathematics (STEM) fields. However, little is known about the evolution of interdisciplinarity in the Social Sciences. Also, how interdisciplinarity and its various aspects evolve over time has seldom been closely quantified and delineated. This paper answers these questions by capturing the disciplinary diversity of the knowledge base of scientific publications in nine broad Social Sciences fields over 55 years. The analysis considers diversity as a whole and its three distinct aspects, namely variety, balance, and disparity. Ordinary least squares (OLS) regressions are also conducted to investigate whether such change, if any, can be found among research with similar characteristics. We find that learning widely and digging deeply have become one of the norms among researchers in Social Sciences. Fields acting as knowledge exporters or independent domains maintain a relatively stable homogeneity in their knowledge base while the knowledge base of importer disciplines evolves towards greater heterogeneity. However, the increase of interdisciplinarity is substantially smaller when controlling for several author and publication related variables.

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Abstract

Purpose This paper analyses the research in Library and Information Science (LIS) and reports on (1) the status of LIS research in 2015 and (2) on the evolution of LIS research longitudinally from 1965 to 2015. Design/methodology/approach The study employs a quantitative intellectual content analysis of articles published in 30+ scholarly LIS journals, following the design by Tuomaala et al. (2014). In the content analysis, we classify articles along eight dimensions covering topical content and methodology. Findings The topical findings indicate that the earlier strong LIS emphasis on L&I services has declined notably, while scientific and professional communication has become the most popular topic. Information storage and retrieval has given up its earlier strong position towards the end of the years analyzed. Individuals are increasingly the units of observation. End-user's and developer's viewpoints have strengthened at the cost of intermediaries' viewpoint. LIS research is methodologically increasingly scattered since survey, scientometric methods, experiment, case studies and qualitative studies have all gained in popularity. Consequently, LIS may have become more versatile in the analysis of its research objects during the years analyzed. Originality/value Among quantitative intellectual content analyses of LIS research, the study is unique in its scope: length of analysis period (50 years), width (8 dimensions covering topical content and methodology) and depth (the annual batch of 30+ scholarly journals).

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Abstract

Empirical evidence demonstrates that citations received by scholarly publications follow a pattern of preferential attachment, resulting in a power-law distribution. Such asymmetry has sparked significant debate regarding the use of citations for research evaluation. However, a consensus has yet to be established concerning the historical trends in citation concentration. Are citations becoming more concentrated in a small number of articles? Or have recent geopolitical and technical changes in science led to more decentralized distributions? This ongoing debate stems from a lack of technical clarity in measuring inequality. Given the variations in citation practices across disciplines and over time, it is crucial to account for multiple factors that can influence the findings. This article explores how reference-based and citation-based approaches, uncited articles, citation inflation, the expansion of bibliometric databases, disciplinary differences, and self-citations affect the evolution of citation concentration. Our results indicate a decreasing trend in citation concentration, primarily driven by a decline in uncited articles, which, in turn, can be attributed to the growing significance of Asia and Europe. On the whole, our findings clarify current debates on citation concentration and show that, contrary to a widely-held belief, citations are increasingly scattered.

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Abstract

Die Szientometrie beschäftigt sich mit der Messbarkeit wissenschaftlicher Leistungen anhand bibliothekarisch nachweisbarer Publikationsergebnisse. Bei genauer Betrachtung ist es ihr Ziel, die Wissenszunahme der Wissenschaft zu messen. Die wissenschaftliche Produktion in Form von Publikationen wächst seit über dreihundert Jahren konstant mit ca. 3,5% pro Jahr. Das entspricht einerVerdopplungsrate von 20 Jahren, die zuerst dem Bibliothekar Fremont Rider 1948 bei Büchern auffiel und die 1963 von Derek J. de Solla Price auch für das Wachstum von Zeitschriften und Bibliografien bestätigt wurde. Die Konstanz dieser Evolution, unabhängig aller sich ereignenden Katastrophen, ist nur zum Teil verstanden, macht aber den unaufhaltsamen Fortschritt der Wissenschaft deutlich. Alle 20 Jahre wird so viel publiziert wie in allen Jahrhunderten davor. Eine etwa gleiche Zunahme verzeichnen die Wissenschaftler, die damit etwa gleich produktiv bleiben. Von ihnen allen sind damit ca. 87% unsere heutigen Zeitgenossen. Aus diesem Wachstum heraus können wir abschätzen, dass in 100.000 laufenden Zeitschriften heute etwa 10 Mio. Publikationen jährlich erscheinen, die von 10 Mio. Wissenschaftlern verfasst werden. Dabei definieren sich nur die als Wissenschaftler, die durchschnittlich eine Publikation jährlich verfassen. Die gesamte Produktion an Buchtiteln, die bisher erschien, dürfte bei etwa 100 Mio. liegen. Davon sind etwa 20 Mio. als wissenschaftlich einzustufen. Wenn folglich 87% aller Wissenschaftler noch heute leben, so betrug die Gesamtzahl der Wissenschaftler in der Welt bisher 11,5 Mio., die in ihrem Leben durchschnittlich 1,5 Bücher pro Kopf verfassten, und etwa das 10-20fache an Zeitschriftenbeiträgen leisteten. Ein Teil dieser Bücher sind allerdings Neuauflagen und Übersetzungen. Nach Lotka, A. J. ist die Produktivität der Wissenschaftler eine schiefe Verteilung von der Form A/n**2, wobei A die Zahl der Autoren mit nur einer Publikation ist und n die Publikationen pro Autor. Während Price in seinen "Networks of Scientific Papers" Vergleichswerte von n**2,5 bis n**3 angab, zeigten Untersuchungen am Science Citation Index (SCI), die auf die gesamte naturwissenschaftliche Literatur hochgerechnet wurden, eher einen Wert von n**1,7. Auf die Tatsache, dass eine Verdopplungsrate der Wissenschaftler von 20 Jahren und eine solche der Menschheit von etwa 50 Jahren dazu führt, dass eines Tages alle Menschen Wissenschaftler werden, hat Price bereits 1963 hingewiesen. Dieser Zustand müsste bei 10 Mio. Wissenschaftlern und 6 Mrd. Menschen in etwa 300 Jahren eintreten, ein nur scheinbar absurder Gedanke, wenn man bedenkt, dass man sich vor 300 Jahren auch kaum vorstellen konnte, dass alle Menschen Lesen, Schreiben und Rechnen lernen können, und dass wir uns ungebildete Menschen immer weniger leisten können.

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Abstract

Purpose This study continues a long history of author co-citation analysis of the intellectual structure of information science into the time period of 2011-2020. It also examines changes in this structure from 2006-2010 through 2011-2015 to 2016-2020. Results will contribute to a better understanding of the information science research field. Design/methodology/approach The well-established procedures and techniques for author co-citation analysis were followed. Full records of research articles in core information science journals published during 2011-2020 were retrieved and downloaded from the Web of Science database. About 150 most highly cited authors in each of the two five-year time periods were selected from this dataset to represent this field, and their co-citation counts were calculated. Each co-citation matrix was input into SPSS for factor analysis, and results were visualized in Pajek. Factors were interpreted as specialties and labeled upon an examination of articles written by authors who load primarily on each factor. Findings The two-camp structure of information science continued to be present clearly. Bibliometric indicators for research evaluation dominated the Knowledge Domain Analysis camp during both fivr-year time periods, whereas interactive information retrieval (IR) dominated the IR camp during 2011-2015 but shared dominance with information behavior during 2016-2020. Bridging between the two camps became increasingly weaker and was only provided by the scholarly communication specialty during 2016-2020. The IR systems specialty drifted further away from the IR camp. The information behavior specialty experienced a deep slump during 2011-2020 in its evolution process. Altmetrics grew to dominate the Webometrics specialty and brought it to a sharp increase during 2016-2020. Originality/value Author co-citation analysis (ACA) is effective in revealing intellectual structures of research fields. Most related studies used term-based methods to identify individual research topics but did not examine the interrelationships between these topics or the overall structure of the field. The few studies that did discuss the overall structure paid little attention to the effect of changes to the source journals on the results. The present study does not have these problems and continues the long history of benchmark contributions to a better understanding of the information science field using ACA.

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Source

Information processing and management. 22(1986), S.417-419