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Nancy J. Nersessian [75]Nancy Joan Nersessian [1]
  1. Creating Scientific Concepts.Nancy J. Nersessian - 2008 - MIT Press.
    How do novel scientific concepts arise? In Creating Scientific Concepts, Nancy Nersessian seeks to answer this central but virtually unasked question in the problem of conceptual change. She argues that the popular image of novel concepts and profound insight bursting forth in a blinding flash of inspiration is mistaken. Instead, novel concepts are shown to arise out of the interplay of three factors: an attempt to solve specific problems; the use of conceptual, analytical, and material resources provided by the cognitive-social-cultural (...)
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  2.  74
    Faraday to Einstein: constructing meaning in scientific theories.Nancy J. Nersessian - 1984 - Hingham, MA: Kluwer Academic Publishers.
    PARTI The Philosophical Situation: A Critical Appraisal We must begin with the mistake and find out the truth in it. That is, we must uncover the source of ...
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  3.  57
    Rethinking Ethnography for Philosophy of Science.Nancy J. Nersessian & Miles MacLeod - 2022 - Philosophy of Science 89 (4):721-741.
    We lay groundwork for applying ethnographic methods in philosophy of science. We frame our analysis in terms of two tasks: to identify the benefits of an ethnographic approach in philosophy of science and to structure an ethnographic approach for philosophical investigation best adapted to provide information relevant to philosophical interests and epistemic values. To this end, we advocate for a purpose-guided form of cognitive ethnography that mediates between the explanatory and normative interests of philosophy of science, while maintaining openness and (...)
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  4. Faraday to Einstein: Constructing Meaning in Scientific Theories.Nancy J. Nersessian - 1987 - British Journal for the Philosophy of Science 38 (4):575-577.
     
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  5.  85
    Empirical Philosophy of Science: Introducing Qualitative Methods into Philosophy of Science.Hanne Andersen, Nancy J. Nersessian & Susann Wagenknecht (eds.) - 2015 - Cham: Springer International Publishing.
    The book examines the emerging approach of using qualitative methods, such as interviews and field observations, in the philosophy of science. Qualitative methods are gaining popularity among philosophers of science as more and more scholars are resorting to empirical work in their study of scientific practices. At the same time, the results produced through empirical work are quite different from those gained through the kind of introspective conceptual analysis more typical of philosophy. This volume explores the benefits and challenges of (...)
  6.  53
    Model-based reasoning in conceptual change.Nancy J. Nersessian - 1999 - In L. Magnani, Nancy Nersessian & Paul Thagard (eds.), Model-Based Reasoning in Scientific Discovery. Kluwer/Plenum. pp. 5--22.
  7. Peeking Inside the Black Box: A New Kind of Scientific Visualization.Michael T. Stuart & Nancy J. Nersessian - 2018 - Minds and Machines 29 (1):87-107.
    Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization that was developed to address just this sort of epistemic opacity. The visualization (...)
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  8.  96
    In the Theoretician's Laboratory: Thought Experimenting as Mental Modeling.Nancy J. Nersessian - 1992 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1992:291 - 301.
    Thought experiments have played a prominent role in numerous cases of conceptual change in science. I propose that research in cognitive psychology into the role of mental modeling in narrative comprehension can illuminate how and why thought experiments work. In thought experimenting a scientist constructs and manipulates a mental simulation of the experimental situation. During this process, she makes use of inferencing mechanisms, existing representations, and general world knowledge to make realistic transformations from one possible physical state to the next. (...)
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  9.  46
    Interdisciplinary problem- solving: emerging modes in integrative systems biology.Miles MacLeod & Nancy J. Nersessian - 2016 - European Journal for Philosophy of Science 6 (3):401-418.
    Integrative systems biology is an emerging field that attempts to integrate computation, applied mathematics, engineering concepts and methods, and biological experimentation in order to model large-scale complex biochemical networks. The field is thus an important contemporary instance of an interdisciplinary approach to solving complex problems. Interdisciplinary science is a recent topic in the philosophy of science. Determining what is philosophically important and distinct about interdisciplinary practices requires detailed accounts of problem-solving practices that attempt to understand how specific practices address the (...)
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  10.  60
    Model-Based Reasoning: Science, Technology, Values.Lorenzo Magnani & Nancy J. Nersessian (eds.) - 2002 - Boston, MA, USA: Kluwer Academic/Plenum Publishers.
    There are several key ingredients common to the various forms of model-based reasoning considered in this book. The term ‘model’ comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations and are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. The book’s contributors are researchers active in the area of creative reasoning in science and technology.
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  11.  94
    Building Simulations from the Ground Up: Modeling and Theory in Systems Biology.Miles MacLeod & Nancy J. Nersessian - 2013 - Philosophy of Science 80 (4):533-556.
    In this article, we provide a case study examining how integrative systems biologists build simulation models in the absence of a theoretical base. Lacking theoretical starting points, integrative systems biology researchers rely cognitively on the model-building process to disentangle and understand complex biochemical systems. They build simulations from the ground up in a nest-like fashion, by pulling together information and techniques from a variety of possible sources and experimenting with different structures in order to discover a stable, robust result. Finally, (...)
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  12.  79
    The cognitive basis of model-based reasoning in science.Nancy J. Nersessian - 2002 - In Peter Carruthers, Stephen Stich & Michael Siegal (eds.), The Cognitive Basis of Science. New York: Cambridge University Press. pp. 133--153.
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  13.  36
    Coupling simulation and experiment: The bimodal strategy in integrative systems biology.Miles MacLeod & Nancy J. Nersessian - 2013 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 44 (4a):572-584.
    The importation of computational methods into biology is generating novel methodological strategies for managing complexity which philosophers are only just starting to explore and elaborate. This paper aims to enrich our understanding of methodology in integrative systems biology, which is developing novel epistemic and cognitive strategies for managing complex problem-solving tasks. We illustrate this through developing a case study of a bimodal researcher from our ethnographic investigation of two systems biology research labs. The researcher constructed models of metabolic and cell-signaling (...)
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  14.  53
    Building Cognition: The Construction of Computational Representations for Scientific Discovery.Sanjay Chandrasekharan & Nancy J. Nersessian - 2015 - Cognitive Science 39 (8):1727-1763.
    Novel computational representations, such as simulation models of complex systems and video games for scientific discovery, are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a theoretical analysis of the cognitive roles such representations play, based on an ethnographic study of the building of computational models in a systems biology laboratory. Specifically, we focus on a case of model-building by an engineer that (...)
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  15. Conceptual change in science and in science education.Nancy J. Nersessian - 1989 - Synthese 80 (1):163 - 183.
    There is substantial evidence that traditional instructional methods have not been successful in helping students to restructure their commonsense conceptions and learn the conceptual structures of scientific theories. This paper argues that the nature of the changes and the kinds of reasoning required in a major conceptual restructuring of a representation of a domain are fundamentally the same in the discovery and in the learning processes. Understanding conceptual change as it occurs in science and in learning science will require the (...)
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  16.  40
    Should physicists preach what they practice?Nancy J. Nersessian - 1995 - Science & Education 4 (3):203-226.
  17. Thought Experimenting as Mental Modeling.Nancy J. Nersessian - 2007 - Croatian Journal of Philosophy 7 (2):125-161.
    The paper argues that the practice of thought experintenting enables scientists to follow through the implications of a way of representing nature by simulating an exemplary or representative situation that is feasible within that representation. What distinguishes thought experimenting from logical argument and other forms of propositional reasoning is that reasoning by means of a thought experiment involves constructing and simulating a mental model of a representative situation. Although thought experimenting is a creative part of scientific practice, it is a (...)
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  18.  61
    Epistemic Identities in Interdisciplinary Science.Lisa M. Osbeck & Nancy J. Nersessian - 2017 - Perspectives on Science 25 (2):226-260.
    Confronting any science studies or learning sciences researcher in the 21st century is the reality of interdisciplinary science. New hybrid fields1 collaboratively build new concepts, combine models from two or more disciplines and forge inter-reliant relationships among specialists with different skill sets to solve new problems. This paper emerges from our recognition that inescapable psychological factors, including identity dynamics, must be described and analyzed in order to better understand the social and cognitive practices specific to interdisciplinary science. In analysis of (...)
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  19.  51
    Modeling complexity: cognitive constraints and computational model-building in integrative systems biology.Miles MacLeod & Nancy J. Nersessian - 2018 - History and Philosophy of the Life Sciences 40 (1):17.
    Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and other tools to (...)
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  20. How Do Engineering Scientists Think? Model‐Based Simulation in Biomedical Engineering Research Laboratories.Nancy J. Nersessian - 2009 - Topics in Cognitive Science 1 (4):730-757.
    Designing, building, and experimenting with physical simulation models are central problem‐solving practices in the engineering sciences. Model‐based simulation is an epistemic activity that includes exploration, generation and testing of hypotheses, explanation, and inference. This paper argues that to interpret and understand how these simulation models function in creating knowledge and technologies requires construing problem solving as accomplished by a researcher–artifact system. It draws on and further develops the framework of “distributed cognition” to interpret data collected in ethnographic and cognitive‐historical studies (...)
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  21.  57
    Modeling systems-level dynamics: Understanding without mechanistic explanation in integrative systems biology.Miles MacLeod & Nancy J. Nersessian - 2015 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 49:1-11.
  22. Philosophy of and as interdisciplinarity.Michael Hg Hoffmann, Jan C. Schmidt & Nancy J. Nersessian - 2013 - Synthese 190 (11):1857-1864.
  23.  33
    The creative industry of integrative systems biology.Miles MacLeod & Nancy J. Nersessian - 2013 - Mind and Society 12 (1):35-48.
    Integrative systems biology is among the most innovative fields of contemporary science, bringing together scientists from a range of diverse backgrounds and disciplines to tackle biological complexity through computational and mathematical modeling. The result is a plethora of problem-solving techniques, theoretical perspectives, lab-structures and organizations, and identity labels that have made it difficult for commentators to pin down precisely what systems biology is, philosophically or sociologically. In this paper, through the ethnographic investigation of two ISB laboratories, we explore the particular (...)
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  24.  51
    The Process of science: contemporary philosophical approaches to understanding scientific practice.Nancy J. Nersessian (ed.) - 1987 - Hingham, MA, USA: Kluwer Academic Publishers.
    ' this volume will make a significant contribution to a more adequate understanding of the 'nature of scientific knowledge and inquiry' ' ISIS Vol.79,No.1,1988.
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  25. Mental Modeling in Conceptual Change.Nancy J. Nersessian - 2010 - International Journal on Humanistic Ideology 3 (1):11-48.
  26.  26
    Aether/Or: The Creation of Scientific Concepts.Nancy J. Nersessian - 1984 - Studies in History and Philosophy of Science Part A 15 (3):175.
  27. Why/How to Study Scientific Thinking.Nancy J. Nersessian - forthcoming - Qualitative Psychology.
    Scientific research is a highly complex and creative domain of human activity. In addition to its intrinsic value, understanding scientific thinking provides insight into the creative potential of human psychological capacities, as they are imbedded in rich social, material, and cultural environments. I discuss findings from my own investigations using two forms of qualitative research suited to studying scientific thinking as situated in context: cognitive-historical and cognitive-ethnographic.
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  28.  57
    Abstraction via generic modeling in concept formation in science.Nancy J. Nersessian - 2005 - Poznan Studies in the Philosophy of the Sciences and the Humanities 86 (1):117-144.
    Cases where analogy has played a significant role in the formation of a new scientific concept are well-documented. Yet, how is it that genuinely new representations can be constructed from existing representations? It is argued that the process of ‘generic modeling’ enables abstraction of features common to both the domain of the source of the analogy and of the target phenomena. The analysis focuses on James Clerk Maxwell's construction of the electromagnetic field concept. The mathematical representation Maxwell constructed turned out (...)
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  29. Interdisciplinarity in the Making: Models and Methods in Frontier Science.Nancy J. Nersessian - 2022 - Cambridge, MA: MIT.
    A cognitive ethnography of how bioengineering scientists create innovative modeling methods. In this first full-scale, long-term cognitive ethnography by a philosopher of science, Nancy J. Nersessian offers an account of how scientists at the interdisciplinary frontiers of bioengineering create novel problem-solving methods. Bioengineering scientists model complex dynamical biological systems using concepts, methods, materials, and other resources drawn primarily from engineering. They aim to understand these systems sufficiently to control or intervene in them. What Nersessian examines here is how cutting-edge bioengineering (...)
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  30.  70
    Diversity as Asset.Andrea Bender, Sieghard Beller & Nancy J. Nersessian - 2015 - Topics in Cognitive Science 7 (4):677-688.
    We begin our commentary by summarizing the commonalities and differences in cognitive phenomena across cultures, as found by the seven papers of this topic. We then assess the commonalities and differences in how our various authors have approached the study of cognitive diversity, and speculate on the need for, and potential of, cross-disciplinary collaboration.
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  31. Nomic concepts, frames, and conceptual change.Hanne Andersen & Nancy J. Nersessian - 2000 - Philosophy of Science 67 (3):241.
    Thomas Kuhn's The Structure of Scientific Revolutions was published at the beginning of what has come to be known as “the cognitive revolution.” With hindsight one can construct significant parallels between the problems of knowledge, perception, and learning with which Kuhn and cognitive scientists were grappling and between the accounts developed by each. However, by and large Kuhn never utilized the research in cognitive science—especially in cognitive psychology—that we believe would have furthered his own paradigm. This is puzzling since he (...)
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  32.  26
    Abstraction via generic modeling in concept formation in science.Nancy J. Nersessian - 2002 - Mind and Society 3 (1):129-154.
    Cases where analogy has played a significant role in the formation of a new scientific concept are well-documented. Yet, how is it that genuinely new representations can be constructed from existing representations? It is argued that the process of ‘generic modeling’ enables abstraction of features common to both the domain of the source of the analogy and of the target phenomena. The analysis focuses on James Clerk Maxwell's construction of the electromagnetic field concept. The mathematical representation Maxwell constructed turned out (...)
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  33.  36
    From Maxwell to Microphysics: Aspects of Electromagnetic Theory in the Last Quarter of the Nineteenth Century. Jed Z. Buchwald.Nancy J. Nersessian - 1987 - Philosophy of Science 54 (3):489-490.
  34.  39
    Situating distributed cognition.Lisa M. Osbeck & Nancy J. Nersessian - 2014 - Philosophical Psychology 27 (1):1-16.
    We historically and conceptually situate distributed cognition by drawing attention to important similarities in assumptions and methods with those of American ?functional psychology? as it emerged in contrast and complement to controlled laboratory study of the structural components and primitive ?elements? of consciousness. Functional psychology foregrounded the adaptive features of cognitive processes in environments, and adopted as a unit of analysis the overall situation of organism and environment. A methodological implication of this emphasis was, to the extent possible, the study (...)
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  35.  55
    Affective problem solving: emotion in research practice.Lisa M. Osbeck & Nancy J. Nersessian - 2011 - Mind and Society 10 (1):57-78.
    This paper presents an analysis of emotional and affectively toned discourse in biomedical engineering researchers’ accounts of their problem solving practices. Drawing from our interviews with scientists in two laboratories, we examine three classes of expression: explicit, figurative and metaphorical, and attributions of emotion to objects and artifacts important to laboratory practice. We consider the overall function of expressions in the particular problem solving contexts described. We argue that affective processes are engaged in problem solving, not as simply tacked onto (...)
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  36.  69
    The distribution of representation.Lisa M. Osbeck & Nancy J. Nersessian - 2006 - Journal for the Theory of Social Behaviour 36 (2):141–160.
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  37. Model‐Based Reasoning in Distributed Cognitive Systems.Nancy J. Nersessian - 2006 - Philosophy of Science 73 (5):699-709.
    This paper examines the nature of model-based reasoning in the interplay between theory and experiment in the context of biomedical engineering research laboratories, where problem solving involves using physical models. These "model systems" are sites of experimentation where in vitro models are used to screen, control, and simulate specific aspects of in vivo phenomena. As with all models, simulation devices are idealized representations, but they are also systems themselves, possessing engineering constraints. Drawing on research in contemporary cognitive science that construes (...)
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  38.  59
    Reasoning from Imagery and Analogy in Scientific Concept Formation.Nancy J. Nersessian - 1988 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1988:41 - 47.
    Concept formation in science is a reasoned process, commensurate with ordinary problem-solving processes. An account of how analogical reasoning and reasoning from imagistic representations generate new scientific concepts is presented. The account derives from case studies of concept formation in science and from computational theories of analogical problem solving in cognitive science. Concept formation by analogy is seen to be a process of increasing abstraction from existing conceptual structures.
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  39. A cognitive-historical approach to meaning in scientific theories.Nancy J. Nersessian - 1987 - In The Process of science: contemporary philosophical approaches to understanding scientific practice. Hingham, MA, USA: Kluwer Academic Publishers.
  40. James Robert Brown: Thought experiments and platonism. Part two.Nancy J. Nersessian, Dunja Jutronic, Ksenija Puskaric, Nenad Miscevic, Andreas K. A. Georgiou & James Robert Brown - 2007 - Croatian Journal of Philosophy 7 (20):125-268.
     
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  41.  33
    Beyond Motivation and Metaphor:'Scientific Passions' and Anthropomorphism.Lisa M. Osbeck & Nancy J. Nersessian - 2013 - In Vassilios Karakostas & Dennis Dieks (eds.), EPSA11 Perspectives and Foundational Problems in Philosophy of Science. Cham: Springer. pp. 455--466.
  42.  22
    Rethinking correspondence: how the process of constructing models leads to discoveries and transfer in the bioengineering sciences.Nancy J. Nersessian & Sanjay Chandrasekharan - 2017 - Synthese 198 (Suppl 21):1-30.
    Building computational models of engineered exemplars, or prototypes, is a common practice in the bioengineering sciences. Computational models in this domain are often built in a patchwork fashion, drawing on data and bits of theory from many different domains, and in tandem with actual physical models, as the key objective is to engineer these prototypes of natural phenomena. Interestingly, such patchy model building, often combined with visualizations, whose format is open to a wide range of choice, leads to the discovery (...)
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  43.  21
    Modeling Practices in Conceptual Innovation.Nancy J. Nersessian - 2012 - In Uljana Feest & Friedrich Steinle (eds.), Scientific Concepts and Investigative Practice. de Gruyter. pp. 245-270.
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  44.  40
    Interdisciplinarities in Action: Cognitive Ethnography of Bioengineering Sciences Research Laboratories.Nancy J. Nersessian - 2019 - Perspectives on Science 27 (4):553-581.
    The paper frames interdisciplinary research as creating complex, distributed cognitive-cultural systems. It introduces and elaborates on the method of cognitive ethnography as a primary means for investigating interdisciplinary cognitive and learning practices in situ. The analysis draws from findings of nearly 20 years of investigating such practices in research laboratories in pioneering bioengineering sciences. It examines goals and challenges of two quite different kinds of integrative problem-solving practices: biomedical engineering (hybridization) and integrative systems biology (collaborative interdependence). Practical lessons for facilitating (...)
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  45.  66
    Why is 'incommensurability' a problem?Nancy J. Nersessian - 1982 - Acta Biotheoretica 31 (4):205-218.
    The origins of the ‘ incommensurability problem’ and its central aspect, the ‘ meaning variance thesis’ are traced to the successive collapse of several distinctions maintained by the standard empiricist account of meaning in scientific theories. The crucial distinction is that between a conceptual structure and a theory. The ‘thesis’ and the ‘problem’ follow from critiques of this distinction by Duhem, Quine and Feyerabend. It is maintained that, rather than revealing the ‘problem’, the arguments leading to it simply show the (...)
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  46.  40
    Child's play.Nancy J. Nersessian - 1996 - Philosophy of Science 63 (4):542-546.
    Although most philosophers are not aware of it, research in cognitive development and in learning in the last decade has made considerable use of the characterizations of the nature and development of scientific knowledge proffered by philosophers of science. In a “reflexive” move, Alison Gopnik proposes philosophers of science can profit from the research of psychologists investigating cognitive development-specifically from that group of researchers who advocate the “theory theory.”.
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  47.  21
    The process of science.Nancy J. Nersessian - 1990 - Erkenntnis 33 (1):121-129.
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  48.  26
    "Why wasn't Lorentz Einstein?" An Examination of the Scientific Method of H. A. Lorentz.Nancy J. Nersessian - 1986 - Centaurus 29 (3):205-242.
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  49.  31
    Forms of Positioning in Interdisciplinary Science Practice and Their Epistemic Effects.Lisa M. Osbeck & Nancy J. Nersessian - 2010 - Journal for the Theory of Social Behaviour 40 (2):136-161.
  50.  18
    Mesoscopic modeling as a cognitive strategy for handling complex biological systems.Miles MacLeod & Nancy J. Nersessian - 2019 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 78:101201.
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