Assignment 1 Working with a Corpus of Science Fiction F26

Exercise:

You will be using a Rmd notebook to do some “distant reading” of a corpus of science fiction. You will follow through the steps of the notebook, review the readings and synthesize your findings about the corpus, as well as bringing a critical eye to the notebook.

• Format: Individual or pairs (maximum 2 people)
• Length: Approximately 1500 words (about an 8-minute read), plus visuals (screenshots or saved images from the notebook)
• Due Date: Saturday, 5 October 2026.
• Medium: a post in Github pages

Corpus:

I have put together a corpus of 1000 works of science fiction from Project Gutenberg. Precisely why so much science fiction is in the Public Domain is unclear, but there is a lot. You can see the metadata table here or in posit.cloud.

This exercise has three main elements:

1. Exploratory Analysis: using digital textual analysis in the notebook “Science Fiction Explorer” (in posit.cloud) to see what kind of exploratory data analysis (EDA) you can do using that corpus. You may do some background research for this on any of the authors in Wikipedia or in the Internet Speculative Fiction Database. Certainly you should take a look at the readings and build connections.
2. Written Synthesis: Assemble your evidence, analysis, and visuals in a web-published essay in the form of a post that tells a coherent story about your findings.
3. Critical Reflection on Moving Beyond the Notebook: Even if you do not know how to do it in practical terms, you should reflect on what kinds of more complex analysis you would like to do with the same corpus if you had more technical capacities.

Readings

(to make reference to in your analysis and critical reflection)

• Underwood, [The Risks of Distant Reading (pp 143-169)] from Ted Underwood’s book Distant Horizons
• Distant Reading: A Conversation with Ama Bemma Adwetewa-Badu (podcast)
• Seed, Science Fiction: A Very Short Introduction

Guiding Questions

(you do not need to answer all these questions):

• Background & Expectations: What did you know about your subject before beginning analysis? What hypotheses did you have about the language contained in the text?

• Computational Insights: What does computational analysis reveal that a linear read would not? Would reading all texts cover-to-cover have been feasible in your timeline? What interesting patterns emerged? Was the notebook interesting, frustrating, limited (or all of the above)?

• Trends & Surprises: What trends can you identify across your corpus? Were there unexpected findings? How do your results compare to your initial hypotheses?

• Methodological Questions: How do the different steps of the notebook represent the data differently? Were there limitations in the tools or approaches you used? What risks are there in reading this way (draw on Underwood)? What other tags would you have liked to see?

• Scope & Scale: How limiting (or enabling) was the idea of working with 1000 texts? What would you analyze differently with more or fewer texts? What subset would you pick and why?

• Transferability: How might you use what you learned from this exercise in other courses, disciplines, or projects like a capstone?

Assessment

Your work will be assessed according to the following criteria located here.

Tips for Success

Writing: Use preview in VSCode to check your Markdown before committing.

Visualization: Make your screenshots speak. Use clear captions that explain what readers are seeing and why it matters to your argument. Your visualizations should support and enhance your analysis, not merely decorate it or fill space. Feel free to annotate on top of the visuals (like putting arrows or circles).

Collaboration: If working in pairs, you may submit a single essay that links to both group members’ sites. Include a brief statement describing each person’s unique contribution to the work.

Publishing: Post your assignment to your course site as a post so instructors and classmates can read and engage with your work.

It is fine to publish your assignment iteratively, but when you finish the final version of your assignment, write at the bottom of it “READY FOR GRADING”.

Good luck with your analysis!