- Added by: Simon Fokt, Contributed by: Matthew Watts
Introduction: Much debate about scientific realism concerns the issue of whether it is compatible with theory change over time. Certain forms of ‘selective realism’ have been suggested with this in mind. Here I consider a closely related challenge for realism: that of articulating how a theory should be interpreted at any given time. In a crucial respect the challenges posed by diachronic and synchronic interpretation are the same; in both cases, realists face an apparent dilemma. The thinner their interpretations, the easier realism is to defend, but at the cost of more substantial commitment. The more substantial their interpretations, the more difficult they are to defend. I consider this worry in the context of the Standard Model of particle physics.
Comment: This text presents challenges to scientific realism, and shows how these challenges can be mitigated.[This is a stub entry. Please add your comments to help us expand it]Export citation in BibTeX formatExport text citationView this text on PhilPapersExport citation in Reference Manager formatExport citation in EndNote formatExport citation in Zotero format
- Added by: Sara Peppe, Contributed by:
Abstract: 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 argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs.
Comment: This article deepens the role of model an data in the scientific investigation taking into account the scientific practice. Obviously, a general framework of the themes the author takes into account is needed.[This is a stub entry. Please add your comments to help us expand it]Export citation in BibTeX formatExport text citationView this text on PhilPapersExport citation in Reference Manager formatExport citation in EndNote formatExport citation in Zotero format