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  • × author_ss:"Sugimoto, C.R."
  • × type_ss:"a"
  • × year_i:[2010 TO 2020}
  1. Demarest, B.; Sugimoto, C.R.: Argue, observe, assess : measuring disciplinary identities and differences through socio-epistemic discourse (2015) 0.02 
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    Abstract
    Calls for interdisciplinary collaboration have become increasingly common in the face of large-scale complex problems (including climate change, economic inequality, and education, among others); however, outcomes of such collaborations have been mixed, due, among other things, to the so-called "translation problem" in interdisciplinary research. This article presents a potential solution: an empirical approach to quantitatively measure both the degree and nature of differences among disciplinary tongues through the social and epistemic terms used (a research area we refer to as discourse epistemetrics), in a case study comparing dissertations in philosophy, psychology, and physics. Using a support-vector model of machine learning to classify disciplines based on relative frequencies of social and epistemic terms, we were able to markedly improve accuracy over a random selection baseline (distinguishing between disciplines with as high as 90% accuracy) as well as acquire sets of most indicative terms for each discipline by their relative presence or absence. These lists were then considered in light of findings of sociological and epistemological studies of disciplines and found to validate the approach's measure of social and epistemic disciplinary identities and contrasts. Based on the findings of our study, we conclude by considering the beneficiaries of research in this area, including bibliometricians, students, and science policy makers, among others, as well as laying out a research program that expands the number of disciplines, considers shifts in socio-epistemic identities over time and applies these methods to nonacademic epistemological communities (e.g., political groups).
  2. Ekbia, H.; Mattioli, M.; Kouper, I.; Arave, G.; Ghazinejad, A.; Bowman, T.; Suri, V.R.; Tsou, A.; Weingart, S.; Sugimoto, C.R.: Big data, bigger dilemmas : a critical review (2015) 0.02 
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    Abstract
    The recent interest in Big Data has generated a broad range of new academic, corporate, and policy practices along with an evolving debate among its proponents, detractors, and skeptics. While the practices draw on a common set of tools, techniques, and technologies, most contributions to the debate come either from a particular disciplinary perspective or with a focus on a domain-specific issue. A close examination of these contributions reveals a set of common problematics that arise in various guises and in different places. It also demonstrates the need for a critical synthesis of the conceptual and practical dilemmas surrounding Big Data. The purpose of this article is to provide such a synthesis by drawing on relevant writings in the sciences, humanities, policy, and trade literature. In bringing these diverse literatures together, we aim to shed light on the common underlying issues that concern and affect all of these areas. By contextualizing the phenomenon of Big Data within larger socioeconomic developments, we also seek to provide a broader understanding of its drivers, barriers, and challenges. This approach allows us to identify attributes of Big Data that require more attention-autonomy, opacity, generativity, disparity, and futurity-leading to questions and ideas for moving beyond dilemmas.

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