---
bibtex: @article{pence2018digital,
title={How to do digital philosophy of science},
author={Pence, Charles H and Ramsey, Grant},
journal={Philosophy of Science},
volume={85},
number={5},
pages={930--941},
year={2018},
publisher={University of Chicago Press Chicago, IL}
}
---
Pence, C. H., & Ramsey, G. (2018). How to do digital philosophy of science. Philosophy of Science, 85(5), 930-941.
Our understanding of science is being broadened by the digitization and automated analysis of the various outputs of the scientific process, such as scientific literature, archival data, and networks of collaboration and correspondence (p1)
digital philosophy of science lets us ask a new class of questions (p1)
Looks at close reading vs distant reading ...
With distant reading, we input a large body of literature into a computer, and use it to do the “reading” for us, extracting large-scale patterns that would be invisible or impractical to find otherwise (p2)
With close reading, a philosopher will have an impressive command over a limited domain (p3)
Because digital philosophy of science is a relatively new field, not only is there no set of standard tools, (p4)
Advantages ...
One of the most significant advantages of distant reading comes from the ability to engage with corpora significantly larger than those usually treated by philosophers and historians of science (p4)
Another advantage comes from the ability of analytical algorithms to parse texts in ways that even well trained close readers cannot (p4)
Tools available (2018) (p6)
- google n-grams
- JSTOR’s Data for Research project
- Indiana Philosophy Ontology project
- Omeka is a free, open-source software product that allows users to construct online archives and museum exhibitions,
- network analysis tools available is Gephi
- If the data to be analyzed is text, a popular choice is Voyant Tools
- RLetters (Pence 2016), available at http://www.rletters.net book level full text search
- R lang & knittr
- figshare
- zenodo
Challenges (p11)
- copyright
- data management
- Reproducibility
Philosophers are not, as a rule, accustomed to producing large amounts of data as part of our research. (p12)
Reproduceability has 3 issues ...
software must be reproducible—that is, easily installed and run by those with the relevant technical expertise (p13)
corpora must be reproducible ... difficult challenge, particularly if one has negotiated access to a body of copyrighted materials for analysis.
original forms of data must be—and remain—available.
A recurring problem with digital humanities results consists in how we can be certain that we have obtained genuine information supporting the conclusions we hope to draw. (p13)
We can in part resolve this by proceeding in an “hypothesis-first” manner— forming clear hypotheses prior to performing analyses (p14)
Many analyses in the digital humanities lack statistical validation, and have only a history of successful use as evidence in their favor (see, e.g., the discussion of validation in Koppel, Schler, and Argamon 2009). (p14)
Pence has recently combined existing work on an episode in the history of biology (Pence 2011) with digital tools (Ramsey and Pence 2016), to produce a more general hypothesis about debates over paradigm change, which is now ripe for a non-digital analysis (Pence in preparation). (p15)