Schedule
Week 1: What even is an introductory graduate course in “DH” in 2019?
Monday, January 14
- The first day of class will feature an introduction to the resources and people at the library by Paige Morgan, Digital Scholarship Librarian and Scholarly Publishing Officer
Readings
DH and Digital Media Studies
- Tara McPherson, “Why are the Digital Humanities So White? or Thinking the Histories of Race and Computation,” from Debates in the Digital Humanities 2012 (2012)
DH in Practice
- Kim Gallon, “Making a Case for the Black Digital Humanities,” from Debates in the Digital Humanities 2016 (2016)
- Miriam Posner, “What’s Next: The Radical, Unrealized Potential of Digital Humanities,” from Debates in the Digital Humanities 2016 (2016)
- Alex Gil and Élika Ortega, “Global Outlooks in Digital Humanities: Multilingual Practices and Minimal Computing,” from Doing Digital Humanities (2016) (B)
- Moya Bailey, Anne Cong-Huyen, Alexis Lothian, and Amanda Phillips, “Reflections on a Movement: #transformDH, Growing Up,” from Debates in the Digital Humanities 2016 (2016)
The Administration of DH
- Rita Raley, “Digital Humanities for the Next Five Minutes,” differences 25.1 (2014) (B)
- Julia Flanders, “Time, Labor, and ‘Alternative Careers’ in Digital Humanities Knowledge Work,” from Debates in the Digital Humanities 2012 (2012)
Week 2: NO CLASS – MLK JR DAY
Monday, January 21
Week 3: Data analysis in the humanities now
Monday, January 28
Readings
- Safiya Noble, “A Society, Searching,” Ch. 1 from Algorithms of Oppression: How Search Engines Reinforce Racism (2018) (B)
- Debates in the Digital Humanities 2016, “Forum: Text Analysis at Scale”. NOTE: Each of these pieces is short, about the length of a blog post.
- Matthew K. Gold and Lauren F. Klein, “Introduction”
- Stephen Ramsay, “Humane Computation”
- Ted Underwood, “Distant Reading and Recent Intellectual History”
- Tanya Clement, “The Ground Truth of DH Text Mining”
- Lisa Marie Rhody, “Why I Dig: Feminist Approaches to Text”
- Tressie McMillan Cottom, “More Scale, More Questions: Observations from Sociology”
- Benjamin M. Schmidt, “Do Humanists Need to Understand Algorithms?”
- Joanna Swafford, “Messy Data and Faulty Tools”
- Alan Liu, “N + 1: A Plea for Cross-Domain Data in the Digital Humanities”
Case study
- INSTRUCTOR: Micki Kaufman, “‘Everything on Paper Will Be Used Against Me’: Quantifying Kissinger”, (2012-present) (watch the video on the homepage and browse the site)
Week 4: Objectivity, quantification, and knowledge
Monday, February 4
- Class will begin with a presentation by Paige Morgan on finding and wrangling humanities data
Readings
- C. P. Snow, “The Two Cultures” (1959) (B)
- Lorraine Daston and Peter Galison, Ch 1 “Epistemologies of the Eye,” from Objectivity (2010) (B)
- Sarah Wilson, “Black Folk by the Numbers: Quantification in Du Bois,” American Literary History 28.1 (2016) (B)
- John Guillory, “The Sokal Affair and the History of Criticism,” Critical Inquiry 28.2 (2002) (B)
No case study this week
Week 5: Reading: close, distant, reductive
Monday, February 11
Due
- Data set for data set analysis assignment
- Install RStudio and R on your machine
- Instructions for Mac
- Make sure to install XQuartz (step 4 in tutorial)
- Instructions for Windows
- Instructions for Mac
- Download tutorials from Github
- Go to the tutorials Github repo
- Click on the green
Clone or Downloadbutton - Click
Download Zip - Save the zip file to your machine, noting where you do so
- Unzip the file
- We will do some exercises involving the command line in class
Readings
- Julie Orlemanski, “Scales of Reading,” Exemplaria 26.2-3 (2014) (B)
- Andrew Piper, “Introduction (Reading’s Refrain),” from Enumerations (2018) (B)
- Sarah Allison, “In Defense of Reading Reductively,” Ch. 1 from Reductive Reading (2018) (B)
Case study
Week 6: Data in the humanities 1: What is data?
Monday, February 18
Due
- Tutorial 1 (upload rendered HTML files to Box “Tutorial 1” folder by class)
Readings
- Daniel Rosenberg, “Data Before the Fact” from “Raw Data” is an Oxymoron (2013) (B)
- Katherine Bode, “The Equivalence of “Close” and “Distant” Reading; or, Toward a New Object for Data-Rich Literary History,” Modern Language Quarterly 78.1 (March 2017) (B)
- Frederick W. Gibbs, “New Forms of History: Critiquing Data and Its Representations,” The American Historian (February 2016)
Case study
- Dieyun, Laura: Lauren Klein, “The Image of Absence: Archival Silence, Data Visualization, and James Hemings” American Literature 85.4 (2013) (B)
Week 7: Data in the humanities 2: What do we do with data?
Monday, February 25
Due
- Tutorial 2 (upload rendered HTML files to Box “Tutorial 2” folder by class)
Readings
- Andrew Goldstone, “Teaching Quantitative Methods: What Makes It Hard (In Literary Studies),” forthcoming in the next edition of Debates in the Digital Humanities (B)
- Sarah Allison, “Other People’s Data: Humanities Edition,” CA: Journal of Cultural Analytics (2016)
- D. Sculley and Bradley M. Pasenek, “Meaning and Mining: the Impact of Implicit Assumptions in Data Mining for the Humanities,” Literary and Linguistic Computing 23.4 (2008) (B)
Case study
- Ashley: Ryan Cordell, “Reprinting, Circulation, and the Network Author in Antebellum Newspapers,” American Literary History 27.3 (August 2015) (B)
- With accompanying methods paper by David Smith, Ryan Cordell, and Abby Mullen, “Computational Methods for Uncovering Reprinted Texts in Antebellum Newspapers” (B)
Week 8: Lab day
Monday, March 4
Due
- Tutorial 3 (upload rendered HTML files to Box “Tutorial 3” folder by class)
In class
- We will discuss all of the tutorials in class today
- Work on data set analysis in remaining time
Friday, March 8
Due
- Data set analysis
Week 9: NO CLASS – SPRING BREAK
March 9 -17
Week 10: Methods 1: Modeling data
Monday, March 18
Readings
- Julia Flanders and Fotis Jannidis, “Data Modeling,” A New Companion to the Digital Humanities (Wiley Blackwell, 2016) (B)
- Richard Jean So, “All Models Are Wrong,” PMLA 132.3 (2017) (B)
- Women Writer’s Project
- Browse the site
- Read about their editorial principles
- Read about their schema customization
- Browse the WWO Lab
- Andrew Piper, “Novel Devotions: Conversional Reading, Computational Modeling, and the Modern Novel,” New Literary History 46.1 (2015) (B)
Case study
- Tarika, Julia: Richard Jean So, Hoyt Long, and Yuancheng Zhu, “Race, Writing, and Computation: Racial Difference and the US Novel, 1880-2000,” Journal of Cultural Analytics, 1.11.19
Week 11: Methods 2: Supervised learning – classifiers
Monday, March 25
Readings
- Hoyt Long and Richard Jean So, “Literary Pattern Recognition: Modernism between Close Reading and Machine Learning,” Critical Inquiry 42.2 (2016) (B)
- Andrew Piper and Eva Portelance, “How Cultural Capital Works: Prizewinning Novels, Bestsellers, and the Time of Reading,” Post45, 5.10.16
Case study
- Carmen: Andrew Piper, Ch. 4 “Fictionality (Sense),” from Enumerations (2018) (B)
Week 12: Methods 3: Unsupervised learning – topic models
Monday, April 1
Due
- Final paper abstract
In class
- UM English graduate student Ruth Trego will visit our class and discuss her experience with collecting data for her dissertation project
Readings
- Megan R. Brett, “Topic Modeling: A Basic Introduction,” Journal Of Digital Humanities 2.1 (Winter 2012)
- Lisa Marie Rhody, “Topic Modeling and Figurative Language,” Journal Of Digital Humanities 2.1 (Winter 2012)
- Andrew Goldstone and Ted Underwood, “The Quiet Transformations of Literary Studies: What Thirteen Thousand Scholars Could Tell Us” (2014) (B)
- See Andrew Goldstone’s site for exploring and visualizing the topic models on which “The Quiet Transformations of Literary Studies” is based: http://www.rci.rutgers.edu/~ag978/quiet/#/model/grid
- Benjamin M. Schmidt, “Words Alone: Dismantling Topic Models in the Humanities,” Journal Of Digital Humanities 2.1 (Winter 2012)
RECOMMENDED:
- If the principles of topic modeling are still unclear, I strongly recommend this recorded presentation, David Mimno, “The Details: Training and Validating Big Models on Big Data” (starting around 3:30)
Case study
- Preston: Andrew Piper, Ch. 3 “Topoi (Dispersion)” from Enumerations (2018) (B)
Week 13: Methods 4: Geographic analysis
Monday, April 8
Readings
- Richard White, “What is Spatial History?”
- Cameron Blevins, “Space, Nation, and the Triumph of Region: A View of the World from Houston,” Journal of American History 101.1 (2014) (B)
- Accompanying digital exhibit, “Mining and Mapping the Production of Space”
Case study
- Miguel: Elizabeth F. Evans and Matthew Wilkens, “Nation, Ethnicity, and the Geography of British Fiction, 1880-1940”, CA: Journal of Cultural Analytics, 7.13.18
- Dataverse DOI
- Jupyter notebooks: Geotypicality, London CA
Week 14: Final paper work
Monday, April 15
Due
- Final paper annotated bibliography
Readings
- Nan Z. Da, “The Computational Case against Computational Literary Studies,” Critical Inquiry 45 (Spring 2019) (B)
- Also read/scan the Appendix, especially section 9, “Suggested Guidelines for Reviewing CLS Manuscripts,” pg 25 (B)
- This is in our course Box folder as a zip file, titled “Da - Appendix.zip.” Download the zip file and unzip to read.
In class
- Catch-up, review, discussion of concepts important to final papers
- Work on final paper
Week 15: Final paper work
Monday, April 22
In class
- Catch-up, review, discussion of concepts important to final papers
- Work on final paper