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  1. La capacidad unificadora de las teorías científicas. Una propuesta alternativa desde el estructuralismo metateórico al enfoque kitchereano de patrones explicativos.Daniel Blanco, Santiago Ginnobili & Pablo Lorenzano - 2019 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 34 (1):111-131.
    Resumen: La capacidad unificadora de una teoría científica es un rasgo usualmente contemplado a la hora de evaluar su adecuación. Kitcher ha elucidado satisfactoriamente tal noción mediante su enfoque de los patrones explicativos. Sin embargo, su perspectiva adolece de ciertas carencias. Concretamente, sostendremos que el requisito de rigurosidad de los patrones para evaluar la capacidad unificadora debe ser repensado, pues atenta contra la heterogeneidad característica de las diferentes aplicaciones de teorías unificadoras. A su vez, mostraremos cómo estas dificultades bien pueden (...)
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  • Consensus versus Unanimity: Which Carries More Weight?Finnur Dellsén - 2021 - British Journal for the Philosophy of Science.
    Around 97% of climate scientists endorse anthropogenic global warming (AGW), the theory that human activities are partly responsible for recent increases in global average temperatures. Clearly, this widespread endorsement of AGW is a reason for non-experts to believe in AGW. But what is the epistemic significance of the fact that some climate scientists do not endorse AGW? This paper contrasts expert unanimity, in which virtually no expert disagrees with some theory, with expert consensus, in which some non-negligible proportion either rejects (...)
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  • The truth of false idealizations in modeling.Uskali Mäki - 2011 - In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.
    Modeling involves the use of false idealizations, yet there is typically a belief or hope that modeling somehow manages to deliver true information about the world. The paper discusses one possible way of reconciling truth and falsehood in modeling. The key trick is to relocate truth claims by reinterpreting an apparently false idealizing assumption in order to make clear what possibly true assertion is intended when using it. These include interpretations in terms of negligibility, applicability, tractability, early-step, and more. Elaborations (...)
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  • How Idealizations Provide Understanding.Michael Strevens - forthcoming - In Stephen Grimm, Christoph Baumberger & Sabine Ammon (eds.), Explaining Understanding: New Essays in Epistemology and the Philosophy of Science. Routledge.
    How can a model that stops short of representing the whole truth about the causal production of a phenomenon help us to understand the phenomenon? I answer this question from the perspective of what I call the simple view of understanding, on which to understand a phenomenon is to grasp a correct explanation of the phenomenon. Idealizations, I have argued in previous work, flag factors that are casually relevant but explanatorily irrelevant to the phenomena to be explained. Though useful to (...)
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  • Addressing the Conflict Between Relativity and Quantum Theory: Models, Measurement and the Markov Property.Gareth Ernest Boardman - 2013 - Cosmos and History 9 (2):86-115.
    Twenty-first century science faces a dilemma. Two of its well-verified foundation stones - relativity and quantum theory - have proven inconsistent. Resolution of the conflict has resisted improvements in experimental precision leaving some to believe that some fundamental understanding in our world-view may need modification or even radical reform. Employment of the wave-front model of electrodynamics, as a propagation process with a Markov property, may offer just such a clarification.
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  • Idealization in mathematics.Thomas Mormann - 2012 - Discusiones Filosóficas 13 (20):147 - 167.
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  • Functional Analyses, Mechanistic Explanations, and Explanatory Tradeoffs.Sergio Daniel Barberis - 2013 - Journal of Cognitive Science 14:229-251.
    Recently, Piccinini and Craver have stated three theses concerning the relations between functional analysis and mechanistic explanation in cognitive sciences: No Distinctness: functional analysis and mechanistic explanation are explanations of the same kind; Integration: functional analysis is a kind of mechanistic explanation; and Subordination: functional analyses are unsatisfactory sketches of mechanisms. In this paper, I argue, first, that functional analysis and mechanistic explanations are sub-kinds of explanation by scientific (idealized) models. From that point of view, we must take into account (...)
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  • Modeling Minimal Conditions for Inequity.Cailin O'Connor - unknown
    This paper describes a class of idealized models that illuminate minimal conditions for inequity. Some such models will track the actual causal factors that generate real world inequity. Others may not. Whether or not these models do track these real-world factors is irrelevant to the epistemic role they play in showing that minimal commonplace factors are enough to generate inequity. In such cases, it is the fact that the model does not fit the world that makes it a particularly powerful (...)
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  • Abstract and Complete.Alkistis Elliott-Graves - unknown
    There are two notions of abstraction that are often confused. The material view implies that the products of abstraction are not concrete. It is vulnerable to the criticism that abstracting introduces misrepresentations to the system, hence abstraction is indistinguishable from idealization. The omission view fares better against this criticism because it does not entail that abstract objects are non-physical and because it asserts that the way scientists abstract is different to the way they idealize. Moreover, the omission view better captures (...)
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  • Constructing diagrams to understand phenomena and mechanisms.Benjamin Sheredos & William Bechtel - manuscript
    Biologists often hypothesize mechanisms to explai phenomena. Our interest is how their understanding of the phenomena and mechanisms develops as they construct diagrams to communicate their claims. We present two case studies in which scientists integrate various data to create a single diagram to communicate their major conclusions in a research publication. In both cases, the history of revisions suggests that scientists' initial drafts encode biases and oversights that are only gradually overcome through prolonged, reflective re-design. To account for this, (...)
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