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  1. 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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  • 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.
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  • Discovering discovery: How faraday found the first metallic colloid.Ryan D. Tweney - 2006 - Perspectives on Science 14 (1):97-121.
    : In 1856, Michael Faraday (1791–1867) conducted nearly a year's worth of research on the optical properties of gold, in the course of which he discovered the first metallic colloids. Following our own discovery of hundreds of the specimens prepared by Faraday for this research, the present paper describes the cognitive role of these "epistemic artifacts" in the dynamics of Faraday's research practices. Analysis of the specimens, Faraday's Diary records, and replications of selected procedures (partly to replace missing kinds of (...)
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  • 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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