Data-Driven Philosophy of Scientific Change

• In Event: Text-Based Machine Learning, Big Data, and the Social Study of Science - II

Abstract

The philosophical project of Scientonomy attempts to provide an empirically adequate account for how and why scientific theories and methods change over time. To do this effectively, it must take into account the various worldviews and epistemic stances of distinct historical communities, meaning that it must be a data-driven historical research program. Most research in historically contextual theories of scientific change rely exclusively on anecdotal historical facts as supporting evidence, and thus fail to have substantial grounding in empirical evidence. One prospective feature of the Scientonomy project, presently in its infancy, is the Tree of Knowledge Database, a semantically enhanced database which seeks to classify the accepted scientific theories and scientific methods of different communities across geography and time, linking them via a unified taxonomy which may be used to characterize the communities’ stances. By constructing such a database, arguments about the general process of scientific change could be guided by empirical data, and once this database is sufficiently complete, more sophisticated data analysis techniques could be used to uncover data trends that may otherwise go unnoticed. Such data trends could then be interpreted in the broader context of scientific change to yield new insights about how scientific change occurs, thus improving our general understanding of the nature of the scientific enterprise.

Authors

• Muhammad Ameer ul Azeem Sarwar, University of Toronto

• Patrick Fraser, University of Toronto