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  1. The Predictive Reframing of Machine Learning Applications: Good Predictions and Bad Measurements.Alexander Martin Mussgnug - 2022 - European Journal for Philosophy of Science 12 (3):1-21.
    Supervised machine learning has found its way into ever more areas of scientific inquiry, where the outcomes of supervised machine learning applications are almost universally classified as predictions. I argue that what researchers often present as a mere terminological particularity of the field involves the consequential transformation of tasks as diverse as classification, measurement, or image segmentation into prediction problems. Focusing on the case of machine-learning enabled poverty prediction, I explore how reframing a measurement problem as a prediction task alters (...)
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  • Model Evaluation: An Adequacy-for-Purpose View.Wendy S. Parker - 2020 - Philosophy of Science 87 (3):457-477.
    According to an adequacy-for-purpose view, models should be assessed with respect to their adequacy or fitness for particular purposes. Such a view has been advocated by scientists and philosophers...
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  • The Epistemic Risk in Representation.Stephanie Harvard & Eric Winsberg - 2022 - Kennedy Institute of Ethics Journal 32 (1):1-31.
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  • Calibrating Statistical Tools: Improving the Measure of Humanity's Influence on the Climate.Corey Dethier - 2022 - Studies in History and Philosophy of Science Part A 94:158-166.
    Over the last twenty-five years, climate scientists working on the attribution of climate change to humans have developed increasingly sophisticated statistical models in a process that can be understood as a kind of calibration: the gradual changes to the statistical models employed in attribution studies served as iterative revisions to a measurement(-like) procedure motivated primarily by the aim of neutralizing particularly troublesome sources of error or uncertainty. This practice is in keeping with recent work on the evaluation of models more (...)
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  • Taming the Tyranny of Scales: Models and Scale in the Geosciences.Alisa Bokulich - 2021 - Synthese 199 (5-6):14167-14199.
    While the predominant focus of the philosophical literature on scientific modeling has been on single-scale models, most systems in nature exhibit complex multiscale behavior, requiring new modeling methods. This challenge of modeling phenomena across a vast range of spatial and temporal scales has been called the tyranny of scales problem. Drawing on research in the geosciences, I synthesize and analyze a number of strategies for taming this tyranny in the context of conceptual, physical, and mathematical modeling. This includes several strategies (...)
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  • Are We in a Sixth Mass Extinction? The Challenges of Answering and Value of Asking.Federica Bocchi, Alisa Bokulich, Leticia Castillo Brache, Gloria Grand-Pierre & Aja Watkins - forthcoming - British Journal for the Philosophy of Science.
    In both scientific and popular circles it is often said that we are in the midst of a sixth mass extinction. Although the urgency of our present environmental crises is not in doubt, such claims of a present mass extinction are highly controversial scientifically. Our aims are, first, to get to the bottom of this scientific debate by shedding philosophical light on the many conceptual and methodological challenges involved in answering this scientific question, and, second, to offer new philosophical perspectives (...)
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  • What is a Data Model?: An Anatomy of Data Analysis in High Energy Physics.Antonis Antoniou - 2021 - European Journal for Philosophy of Science 11 (4):1-33.
    Many decades ago Patrick Suppes argued rather convincingly that theoretical hypotheses are not confronted with the direct, raw results of an experiment, rather, they are typically compared with models of data. What exactly is a data model however? And how do the interactions of particles at the subatomic scale give rise to the huge volumes of data that are then moulded into a polished data model? The aim of this paper is to answer these questions by presenting a detailed case (...)
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