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  1. Principled Mechanistic Explanations in Biology: A Case Study of Alzheimer's Disease.Sepehr Ehsani - manuscript
    Following an analysis of the state of investigations and clinical outcomes in the Alzheimer's research field, I argue that the widely-accepted 'amyloid cascade' mechanistic explanation of Alzheimer's disease appears to be fundamentally incomplete. In this context, I propose that a framework termed 'principled mechanism' (PM) can help with remedying this problem. First, using a series of five 'tests', PM systematically compares different components of a given mechanistic explanation against a paradigmatic set of criteria, and hints at various ways of making (...)
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  • Multiple-Models Juxtaposition and Trade-Offs among Modeling Desiderata.Yoshinari Yoshida - 2021 - Philosophy of Science 88 (1):103-123.
    This article offers a characterization of what I call multiple-models juxtaposition, a strategy for managing trade-offs among modeling desiderata. MMJ displays models of distinct phenomena to...
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  • Philosophy of Cell Biology.William Bechtel & Andrew Bollhagen - 2019 - Stanford Encyclopedia of Philosophy.
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  • (1 other version)What distinguishes data from models?Sabina Leonelli - 2019 - European Journal for Philosophy of Science 9 (2):22.
    I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...)
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  • Thinking about idealization, abstraction and scientific models: an introduction.Demetris Portides - 2021 - Synthese 198 (Suppl 24):5849-5853.
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  • (1 other version)What distinguishes data from models?Sabina Leonelli - 2019 - European Journal for Philosophy of Science 9 (2):22.
    I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...)
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