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Kuhn used the duck–rabbit figure as a metaphor for revolutionary change in science. The two aspects of the drawing stand for two ways of perceiving the world, before and after a revolution, while the drawing itself represents the world that, paradoxically, both changes and remains the same. I argue that Kuhn, aiming to bring about a revolution in philosophy of science, did not want to eliminate the paradox, but rather to exploit and underscore it to challenge what he saw as the dominant epistemological paradigm. I also argue that Kuhn rejected the two-tiered view of perception, first observing and then interpreting raw data, in favour of the theory-ladenness of observation that echoed Wittgenstein’s account of ‘seeing’ rather than Hanson’s assimilation of scientific observation to ‘seeing as’. According to Wittgenstein, ‘seeing as’ is parasitic upon regular seeing and, in that sense, cannot illuminate the general case of perception in science. Finally, I show that the analogy between the dawning of an aspect and novelty in science, informed Kuhn’s views on creativity. In his view, advances in science emerge when a system of beliefs is transformed by new patterns of organization.
Interpreting Kuhn provides a comprehensive, up-to-date study of Thomas Kuhn's philosophy and legacy. With twelve essays newly written by an international group of scholars, it covers a wide range of topics where Kuhn had an influence. Part I deals with foundational issues such as Kuhn's metaphysical assumptions, his relationship to Kant and Kantian philosophy, as well as contextual influences on his writing, including Cold War psychology and art. Part II tackles three Kuhnian concepts: normal science, incommensurability, and scientific revolutions. Part III deals with the Copernican Revolution in astronomy, the theory-ladenness of observation, scientific discovery, Kuhn's evolutionary analogies, and his theoretical monism. The volume is an ideal resource for advanced students seeking an overview of Kuhn's philosophy, and for specialists following the development of Kuhn scholarship.
Big Data and methods for analyzing large data sets such as machine learning have in recent times deeply transformed scientific practice in many fields. However, an epistemological study of these novel tools is still largely lacking. After a conceptual analysis of the notion of data and a brief introduction into the methodological dichotomy between inductivism and hypothetico-deductivism, several controversial theses regarding big data approaches are discussed. These include, whether correlation replaces causation, whether the end of theory is in sight and whether big data approaches constitute entirely novel scientific methodology. In this Element, I defend an inductivist view of big data research and argue that the type of induction employed by the most successful big data algorithms is variational induction in the tradition of Mill's methods. Based on this insight, the before-mentioned epistemological issues can be systematically addressed.
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