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  1. Non-Measurability, Imprecise Credences, and Imprecise Chances.Yoaav Isaacs, Alan Hájek & John Hawthorne - 2021 - Mind 131 (523):892-916.
    – We offer a new motivation for imprecise probabilities. We argue that there are propositions to which precise probability cannot be assigned, but to which imprecise probability can be assigned. In such cases the alternative to imprecise probability is not precise probability, but no probability at all. And an imprecise probability is substantially better than no probability at all. Our argument is based on the mathematical phenomenon of non-measurable sets. Non-measurable propositions cannot receive precise probabilities, but there is a natural (...)
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  • A Framework for Pragmatic Reliability.Isaac Davis - 2020 - Philosophy of Science 87 (4):704-726.
    I propose a framework for pragmatic reliability in-the-limit criteria, extending the epistemic reliability framework. I identify some common scientific contexts that complicate the application or i...
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  • Belief Revision Theory.Hanti Lin - 2019 - In Richard Pettigrew & Jonathan Weisberg (eds.), The Open Handbook of Formal Epistemology. PhilPapers Foundation. pp. 349-396.
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  • Towards a logic for ‘because’.Eric Raidl & Hans Rott - 2024 - Philosophical Studies 181 (9):2247-2277.
    This paper explores the connective ‘because’, based on the idea that ‘_C_ because _A_’ implies the acceptance/truth of the antecedent _A_ as well as of the consequent _C_, and additionally that the antecedent makes a difference for the consequent. To capture this idea of difference-making a ‘relevantized’ version of the Ramsey Test for conditionals is employed that takes the antecedent to be relevant to the consequent in the following sense: a conditional is true/accepted in a state \(\sigma \) just in (...)
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  • How Theories of Induction Can Streamline Measurements of Scientific Performance.Slobodan Perović & Vlasta Sikimić - 2020 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 51 (2):267-291.
    We argue that inductive analysis and operational assessment of the scientific process can be justifiably and fruitfully brought together, whereby the citation metrics used in the operational analysis can effectively track the inductive dynamics and measure the research efficiency. We specify the conditions for the use of such inductive streamlining, demonstrate it in the cases of high energy physics experimentation and phylogenetic research, and propose a test of the method’s applicability.
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