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  1. Evidence for personalised medicine: mechanisms, correlation, and new kinds of black box.Mary Jean Walker, Justin Bourke & Katrina Hutchison - 2019 - Theoretical Medicine and Bioethics 40 (2):103-121.
    Personalised medicine has been discussed as a medical paradigm shift that will improve health while reducing inefficiency and waste. At the same time, it raises new practical, regulatory, and ethical challenges. In this paper, we examine PM strategies epistemologically in order to develop capacities to address these challenges, focusing on a recently proposed strategy for developing patient-specific models from induced pluripotent stem cells so as to make individualised treatment predictions. We compare this strategy to two main PM strategies—stratified medicine and (...)
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  • (1 other version)Collective responsibility and fraud in scientific communities.Bryce Huebner & Liam Kofi Bright - 2020 - In Saba Bazargan-Forward & Deborah Tollefsen (eds.), The Routledge Handbook of Collective Responsibility. Routledge.
    Given the importance of scientific research in shaping our perception of the world, and our senses of what policies will and won’t succeed in altering that world, it is of great practical, political, and moral importance that we carry out scientific research with integrity. The phenomenon of scientific fraud stands in the way of that, as scientists may knowingly enter claims they take to be false into the scientific literature, often knowingly doing so in defiance of norms they profess allegiance (...)
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  • Accountability and values in radically collaborative research.Eric Winsberg, Bryce Huebner & Rebecca Kukla - 2014 - Studies in History and Philosophy of Science Part A 46:16-23.
    This paper discusses a crisis of accountability that arises when scientific collaborations are massively epistemically distributed. We argue that social models of epistemic collaboration, which are social analogs to what Patrick Suppes called a “model of the experiment,” must play a role in creating accountability in these contexts. We also argue that these social models must accommodate the fact that the various agents in a collaborative project often have ineliminable, messy, and conflicting interests and values; any story about accountability in (...)
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  • Getting to Know the World Scientifically: An Objective View.Paul Needham - 2020 - Cham, Schweiz: Springer.
    This undergraduate textbook introduces some fundamental issues in philosophy of science for students of philosophy and science students. The book is divided into two parts. Part 1 deals with knowledge and values. Chap. 1 presents the classical conception of knowledge as initiated by the ancient Greeks and elaborated during the development of science, introducing the central concepts of truth, belief and justification. Aspects of the quest for objectivity are taken up in the following two chapters. Moral issues are broached in (...)
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  • Prediction in context: On the comparative epistemic merit of predictive success.Martin Carrier - 2014 - Studies in History and Philosophy of Science Part A 45:97-102.
    The considerations set out in the paper are intended to suggest that in practical contexts predictive power does not play the outstanding roles sometimes accredited to it in an epistemic framework. Rather, predictive power is part of a network of other merits and achievements. Predictive power needs to be judged differently according to the specific conditions that apply. First, predictions need to be part of an explanatory framework if they are supposed to guide actions reliably. Second, in scientific expertise, the (...)
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  • Calibrating and constructing models of protein folding.Jeffry L. Ramsey - 2007 - Synthese 155 (3):307-320.
    Prediction is more than testing established theory by examining whether the prediction matches the data. To show this, I examine the practices of a community of scientists, known as threaders, who are attempting to predict the final, folded structure of a protein from its primary structure, i.e., its amino acid sequence. These scientists employ a careful and deliberate methodology of prediction. A key feature of the methodology is calibration. They calibrate in order to construct better models. The construction leads to (...)
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