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Aspects of theory-ladenness in data-intensive science

Pietsch, Wolfgang (2014) Aspects of theory-ladenness in data-intensive science. In: UNSPECIFIED.

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Abstract

Recent claims, mainly from computer scientists, concerning a largely automated and model-free data-intensive science have been countered by critical reactions from a number of philosophers of science. The debate suffers from a lack of detail in two respects, regarding (i) the actual methods used in data-intensive science and (ii) the specific ways in which these methods presuppose theoretical assumptions. I examine two widely-used algorithms, classificatory trees and non-parametric regression, and argue that these are theory-laden in an external sense, regarding the framing of research questions, but not in an internal sense concerning the causal structure of the examined phenomenon. With respect to the novelty of data-intensive science, I draw an analogy to exploratory as opposed to theory-directed experimentation.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Creators:
CreatorsEmailORCID
Pietsch, Wolfgangpietsch@cvl-a.tum.de
Keywords: Data-intensive science Theory-ladenness Exploratory experimentation Eliminative induction Non-parametric modeling Big Data
Subjects: General Issues > Causation
Specific Sciences > Complex Systems
Specific Sciences > Computer Science
Specific Sciences > Computer Science > Artificial Intelligence
General Issues > Confirmation/Induction
General Issues > Experimentation
General Issues > Models and Idealization
Specific Sciences > Probability/Statistics
General Issues > Structure of Theories
General Issues > Technology
Depositing User: Wolfgang Pietsch
Date Deposited: 24 Jun 2014 14:56
Last Modified: 24 Jun 2014 14:56
Item ID: 10777
Subjects: General Issues > Causation
Specific Sciences > Complex Systems
Specific Sciences > Computer Science
Specific Sciences > Computer Science > Artificial Intelligence
General Issues > Confirmation/Induction
General Issues > Experimentation
General Issues > Models and Idealization
Specific Sciences > Probability/Statistics
General Issues > Structure of Theories
General Issues > Technology
Date: 1 March 2014
URI: http://philsci-archive.pitt.edu/id/eprint/10777

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