David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Synthese 182 (1):57-71 (2011)
The last two decades have seen a rising interest in (a) the notion of a scientific phenomenon as distinct from theories and data, and (b) the intricacies of experimentally producing and stabilizing phenomena. This paper develops an analysis of the stabilization of phenomena that integrates two aspects that have largely been treated separately in the literature: one concerns the skills required for empirical work; the other concerns the strategies by which claims about phenomena are validated. I argue that in order to make sense of the process of stabilization, we need to distinguish between two types of phenomena: phenomena as patterns in the data ( surface regularities ) and phenomena as underlying (or hidden ) regularities. I show that the epistemic relationships that data bear to each of these types of phenomena are different: Data patterns are instantiated by individual data, whereas underlying regularities are indicated by individual data, insofar as they instantiate a data pattern. Drawing on an example from memory research, I argue that neither of these two kinds of phenomenon can be stabilized in isolation. I conclude that what is stabilized when phenomena are stabilized is the fit between surface regularities and hidden regularities
|Keywords||Data Phenomena Stabilization Validation Bogen/Woodward Hacking Epistemology of experimentation|
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References found in this work BETA
Thomas S. Kuhn (1996). The Structure of Scientific Revolutions. University of Chicago Press.
Thomas S. Kuhn (1962). The Structure of Scientific Revolutions Vol. The University of Chicago Press.
Nelson Goodman (1983). Fact, Fiction, and Forecast. Harvard University Press.
H. M. Collins (1985). Changing Order: Replication and Induction in Scientific Practice. University of Chicago Press.
David Zaret (1977). The Essential Tension: Selected Studies in Scientific Tradition and Change. [REVIEW] Philosophical Review 90 (1):146-149.
Citations of this work BETA
Uljana Feest (2011). Remembering (Short-Term) Memory: Oscillations of an Epistemic Thing. Erkenntnis 75 (3):391-411.
Lara Huber & Lara K. Keuck (2013). Mutant Mice: Experimental Organisms as Materialised Models in Biomedicine. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 44 (3):385-391.
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