CfA: Big Data and the History and Philosophy of Science
Keynote Speakers: Pieter Francois (University of Oxford), Rachel Spicer (London School of Economics), Charles Pence (UC Louvain)
Philosophers and
historians of science have long been wary about the uses of individual case
studies to evaluate philosophical claims about science. The possibilities of
cherry-picking or shoehorning in preconceived assumptions about scientific
practice into carefully selected examples have led to serious concerns about
the prospects of fruitful ways of testing general claims about the process of
scientific change. The aim of the conference is to bring together an
interdisciplinary array of scholars from philosophy, history, computer science,
AI and deep learning, information science, and the social sciences to discuss
the problems and prospects with using various big data approaches in the field
of the history and philosophy of science.
With the rise of
the digital humanities and the development of a variety of complementary
computer-aided techniques (e.g. distant reading, topic modelling, corpus
analytics), big-data approaches have become more common in several
subdisciplines of history and the humanities. Specifically, they have been used
prominently in two recent projects that will be represented and discussed by
our first two keynote speakers: the Database of Religious History and Seshat:
Global History Databank. The success and potential demonstrated by these
projects suggests the benefits of these methods for the history of science.
While numerous groups are working on digital humanities/HPS projects with new
AI-based tools (e.g. Gingras and Guay 2011), there remain outstanding issues to
be addressed to develop publicly accessible, centralized databases that can
provide an up-to-date synthesis of scholarly research for specialists and
non-specialists alike.
Such databases
raise all sorts of issues. Specifically, many questions concerning the
identification, reliable extraction, and pattern analysis of historical data
need to be addressed. A few, specific examples include:
· What are the
challenges of constructing historical databases? How can we build and justify
their ontologies? How are key historical variables selected?
· Can deep machine
learning or AI techniques expunge helpful data from primary historical texts?
Should these tools be only used on primary texts or secondary texts as
well?
· Are there limits
as to what big data approaches can teach us about the history of science? If
so, what are these limitations?
· Can there be a
unified vocabulary to identify and define data points across diverse historical
episodes? What’s the relation between local vocabularies of actor’s categories
and those of historians? How can both be captured while avoiding
anachronisms?
· How is the
imprecision, incompleteness, and uncertainty of historical data best
represented? Is there a substantial difference between inferred and non-inferred
historical data? How can differences in historical interpretation best be
conceptualized?
· Can historical
data be used to derive and justify claims about various historical trends and
patterns? How can computational techniques detect patterns and test hypotheses
concerning, e.g., the co-evolution of theories, methods, values, and practices,
or the composition of scientific communities and their dynamics?
Please submit a 500-word abstract by Google Form by
January 15th, 2023. Communication of acceptance will be by March
2023. Please note that the conference aims to be both in-person and online (for
those participants who cannot make it to Toronto). However, there remains an
open possibility that the event will be hosted fully online.
Conference Website
Organizing Committee
· Jamie Shaw
(Leibniz Universität Hannover)
· Hakob Barseghyan
(University of Toronto)
· Benjamin Goldberg
(University of South Florida)
· Gregory Rupik
(University of Toronto)