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  1. Model-Selection Theory: The Need for a More Nuanced Picture of Use-Novelty and Double-Counting.Katie Steele & Charlotte Werndl - 2016 - British Journal for the Philosophy of Science:axw024.
    This article argues that common intuitions regarding (a) the specialness of ‘use-novel’ data for confirmation and (b) that this specialness implies the ‘no-double-counting rule’, which says that data used in ‘constructing’ (calibrating) a model cannot also play a role in confirming the model’s predictions, are too crude. The intuitions in question are pertinent in all the sciences, but we appeal to a climate science case study to illustrate what is at stake. Our strategy is to analyse the intuitive claims in (...)
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  • Accommodation, prediction and replication: model selection in scale construction.Clayton Peterson - 2019 - Synthese 196 (10):4329-4350.
    In psychology, measurement instruments are constructed from scales, which are obtained on the grounds of exploratory and confirmatory factor analysis. Looking at the literature, one can find various recommendations regarding how these techniques should be used during the scale construction process. Some authors suggest to use exploratory factor analysis on the entire data set while others advice to perform an internal cross-validation by randomly splitting the data set in two and then either perform exploratory factor analysis on both parts or (...)
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  • Underdetermination, Black Boxes, and Measurement.Teru Miyake - 2013 - Philosophy of Science 80 (5):697-708.
    This article introduces the notion of a kind of inference called black box measurement and argues that it is both historically and philosophically significant. Thinking about certain classic cases of underdetermination using this notion can give us a better understanding of how these cases are resolved. I take the main philosophical problem of black box measurement to be the justification of assumptions that are needed in order to make these measurements. I sketch some ways in which such enabling assumptions might (...)
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  • Evidentiary inference in evolutionary biology: Review of Elliott Sober’s Evidence and evolution: the logic behind the science. Cambridge University Press, New York.James Justus - 2011 - Biology and Philosophy 26 (3):419-437.
  • Counterexamples to a likelihood theory of evidence.Malcolm R. Forster - 2006 - Minds and Machines 16 (3):319-338.
    The likelihood theory of evidence (LTE) says, roughly, that all the information relevant to the bearing of data on hypotheses (or models) is contained in the likelihoods. There exist counterexamples in which one can tell which of two hypotheses is true from the full data, but not from the likelihoods alone. These examples suggest that some forms of scientific reasoning, such as the consilience of inductions (Whewell, 1858. In Novum organon renovatum (Part II of the 3rd ed.). The philosophy of (...)
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  • How autism shows that symptoms, like psychiatric diagnoses, are 'constructed': methodological and epistemic consequences.Sam Fellowes - 2021 - Synthese 199 (1-2):4499-4522.
    Critics who are concerned over the epistemological status of psychiatric diagnoses often describe them as being constructed. In contrast, those critics usually see symptoms as relatively epistemologically unproblematic. In this paper I show that symptoms are also constructed. To do this I draw upon the demarcation between data and phenomena. I relate this distinction to psychiatry by portraying behaviour of individuals as data and symptoms as phenomena. I then draw upon philosophers who consider phenomena to be constructed to argue that (...)
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