[SIGCIS-Members] Book Announcement: Critical Digital Humanities

James E. Dobson James.E.Dobson at dartmouth.edu
Thu Mar 28 06:02:13 PDT 2019


Dear SIGCIS Folks,

It was a real pleasure meeting everyone in St. Louis! I thought I'd 
share an announcement for my latest book, Critical Digital Humanities: 
The Search for a Methodology. It was just published by the University of 
Illinois Press as part of the "Topics in the Digital Humanities Series."

https://www.press.uillinois.edu/books/catalog/48xfp2zp9780252042270.html

If you want to purchase it, you can use the offer code S19UIP on IUP's 
website to get 30% off the paperback, hardcover, and ebook formats.

The final chapter of the book gives what I'm calling the "intellectual 
history" of an algorithm, k-nearest neighbor. The abstract for this 
chapter, which would be the one of most interest to this community, 
reads as such:

This chapter turns to a lower level of computation to produce a cultural 
critique and historicization of one of the most important algorithms 
used in digital humanities and other big data applications in the 
present moment, the k-Nearest Neighbor or k-NN algorithm. The chapter 
reconstructs the partial genealogy, the intellectual history, of this 
important algorithm that was key to sense making in the mid-twentieth 
century and has found continued life in the twenty-first century. In 
both its formalized description, its exposition in the papers 
introducing and refining the rule and its implementation in algorithmic 
form, and in its actual use, the k-nearest neighbor algorithm draws on 
dominant mid-twentieth century ideologies and tropes, including 
partitioning, segregation, suburbanization, and democratization. In the 
process of situating the k-NN algorithm within the larger field 
containing other residual and emergent statistical methods, the author 
seeks to produce an intervention within the developing critical theory 
of algorithmic governmentality.

My present book project continues this line of inquiry by focusing on 
computer vision algorithms and has been greatly influenced by the 
SHOT/SIGCIS community and many discussions had at the last conference.


Best,

Jed

-- 
James E. Dobson, Ph.D.
Dartmouth College
420 Moore Hall
http://www.dartmouth.edu/~jed


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