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Knowledge, politics, and commerce: Science under the pressure of practice

In M. Carrier & A. Nordmann (eds.), Science in the Context of Application. Springer. pp. 11--30 (2011)

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  1. Modelling Beyond Application: Epistemic and Non-epistemic Values in Modern Science.Ekaterina Svetlova - 2014 - International Studies in the Philosophy of Science 28 (1):79-98.
    In recent years, philosophers of science have begun to realize that the clear separation of the creation of models in academia and the application of models outside science is not possible. When these philosophers address hybrid contexts in which science is entwined with policy, business, and other realms of society, these often practically oriented realms no longer represent ‘the surroundings’ of science but rather are considered an essential part of it. I argue—and demonstrate empirically—that the judgement of a theory or (...)
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  • A Tapestry of Values: Response to My Critics.Kevin C. Elliott - 2018 - Philosophy, Theory, and Practice in Biology 10 (11).
    This response addresses the excellent responses to my book provided by Heather Douglas, Janet Kourany, and Matt Brown. First, I provide some comments and clarifications concerning a few of the highlights from their essays. Second, in response to the worries of my critics, I provide more detail than I was able to provide in my book regarding my three conditions for incorporating values in science. Third, I identify some of the most promising avenues for further research that flow out of (...)
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  • Inductive risk: does it really refute value-freedom?Markus Dressel - 2022 - Theoria 37 (2):181-207.
    The argument from inductive risk is considered to be one of the strongest challenges for value-free science. A great part of its appeal lies in the idea that even an ideal epistemic agent—the “perfect scientist” or “scientist qua scientist”—cannot escape inductive risk. In this paper, I scrutinize this ambition by stipulating an idealized Bayesian decision setting. I argue that inductive risk does not show that the “perfect scientist” must, descriptively speaking, make non-epistemic value-judgements, at least not in a way that (...)
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