Visualizing and tracking the growth of competing paradigms: Two case studies

Chen, Chaomei; Cribbin, Timothy; Macredie, Robert D.; Morar, Sonali

doi:10.1002/asi.10075

Cited by 73 publications

(50 citation statements)

References 29 publications

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“…One of the most common structuring methods is the co-citation analysis. Much work has been done developing the theory and methodology of author co-citation analysis, firmly establishing it as a bibliometric research tool, realizing that new improvements on this methodology keep on coming out (Ahlgren, Jarneving, and Rousseau, 2003;Chen, Cribbin, and Macredie, 2002;White, 2003aWhite, , 2003b; and the special issue of the Journal of the American Society for Information Science and Technology, 2003). There are proposals arguing that author co-citation maps provide useful insights from which the intellectual structure of a field and the communication networks within it can be observed (McCain, 1990) assuming that bibliographic elements act as concept surrogates (Small, 2003).…”

Section: The Co-citation Methodsmentioning

confidence: 99%

The resource‐based theory: dissemination and main trends

Acedo

Barroso

Galán-González

2006

Strategic Management Journal

467

268

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Section: The Co-citation Methodsmentioning

confidence: 99%

The resource‐based theory: dissemination and main trends

Acedo

Barroso

Galán-González

2006

Strategic Management Journal

467

268

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“…[3][4][5][6][7][8][9][10] Related issues have been also addressed by researchers over the last decade. [11][12][13][14][15] In this article, we will describe an integrative approach to tracing knowledge diffusion based on progressive and explanatory visualization of the evolution of patent citation networks.…”

Section: Scientometrics 59 (2004)mentioning

confidence: 99%

Tracing knowledge diffusion

2004

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Knowledge diffusion is the adaptation of knowledge in a broad range of scientific and engineering research and development. Tracing knowledge diffusion between science and technology is a challenging issue due to the complexity of identifying emerging patterns in a diverse range of possible processes. In this article, we describe an approach that combines complex network theory, network visualization, and patent citation analysis in order to improve the means for the study of knowledge diffusion. In particular, we analyze patent citations in the field of tissue engineering. We emphasize that this is the beginning of a longer-term endeavor that aims to develop and deploy effective, progressive, and explanatory visualization techniques for us to capture the dynamics of the evolution of patent citation networks. The work has practical implications on resource allocation, strategic planning, and science policy.

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“…It emphasizes the importance of information visualization techniques and readily available data such as the citation databases compiled by the Institute for Scientific Information (ISI). Recently, an increasing number of KDV studies began to emerge in a variety of knowledge domains, including information science (White & McCain, 1998), computer graphics (Chen, Paul, & O'Keefe, 2001), mass extinction (Chen, Cribbin, Macredie, & Morar, 2002), mad cow disease Chen, Kuljius, & Paul, 2001), and visual interfaces of digital libraries (Boyack, Wylie, & Dividson, 2002).…”

Section: Introductionmentioning

confidence: 99%

The rising landscape: A visual exploration of superstring revolutions in physics

Chen

Kuljis

2003

J. Am. Soc. Inf. Sci.

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Knowledge domain visualization is a visual exploratory approach to the study of the development of a knowledge domain. In this study, we focus on the practical issues concerning modeling and visualizing scientific revolutions. We study the growth patterns of specialties derived from citation and cocitation data on string theory in physics. Special attention is given to the two superstring revolutions since the 1980s. The superstring revolutions are visualized, animated, and analyzed using the general framework of Thomas Kuhn's structure of scientific revolutions. The implications of taking this approach are discussed. IntroductionCapturing and understanding the dynamics of a scientific revolution is a daunting challenge due to the complexity, the diversity, and the scale of the contributing factors and relationships involved. Thomas Kuhn's structure of scientific revolutions represents an influential work in the field. His notion of paradigm shift has become widely known in a diverse range of scientific disciplines. However, its use is limited and time consuming because scientists, especially junior scientists, often have to sift through mountains of publications and other documents to digest and synthesize a wide variety of diverse views into a clear picture of a scientific revolution. One way to simplify this process is by constructing such pictures automatically based on some publicly accessible data.A paradigm is a world view of underlying theories and methodologies that the members of a scientific community share (Kuhn, 1996). According to Kuhn, a scientific discipline evolves through a high-level state transition process, namely: normal science 3 crisis 3 scientific revolutions 3 normal science. A normal science period is typically predominated by a stable paradigm. The core of the dominating paradigm is not seriously challenged in this period. In contrast, a crisis period is marked by fundamental challenges to the dominating paradigm due to anomalies that cannot be simply explained away within the existing paradigm. New paradigms of a greater explanation power begin to challenge the authoritative position of the dominating paradigm. A scientific revolution takes place when the current dominating paradigm gives way to a new paradigm.In looking at paradigm shifts researchers have attempted to facilitate the process of understanding the difference between competing theories in terms of their conceptual structures. Paul Thagard (1992) suggests that explanation coherence holds the key for theory selection in science. A theory is more likely to gain acceptance if it has greater explanation coherence than its competitors. To study conceptual revolutions, Thagard designed a computational method that can compare the structural characteristics of conceptual systems before, during, and after conceptual revolutions. He conducted several case studies of conceptual revolutions using this method. Thagard's method is based on specific assumptions and major assertions about a theory. This strict dependency becomes a doubl...

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Visualizing and tracking the growth of competing paradigms: Two case studies

Cited by 73 publications

References 29 publications

The resource‐based theory: dissemination and main trends

The resource‐based theory: dissemination and main trends

Tracing knowledge diffusion

The rising landscape: A visual exploration of superstring revolutions in physics

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