Overview

Assignment 1 invites you to download some data about a country you know something about and to filter that dataset such that you show some of its features of interest to you. This assignment builds on concepts and tools we’ve discussed in class and a “computational notebook” in posit.cloud. Not only will you create a map of the features, but you will be also asked to comment on the visualization you create using the readings from the course.

• Type: Individual
• Length: Approximately 1500 words (about an 8-minute read), plus maps
• Format: This assignment will be completed in Markdown and posted on your individual Github pages site.
• Due Date: 28 September 2026, 11:59pm

Three Main Elements

This assignment has five core components:

1. Downloading or querying data: You will acquire data from open datasets on the web about a place of interest to you in the world. I would recommend that you choose a region of a country, rather than a city or a very large country. This suggestion is to limit the amount of data you have to work with.
2. Choosing the features of interest to you: I would recommend that you use the country download from GeoNames to begin this assignment. Know that datasets for the same place will be very different!
3. Filtering for these Features: Use a combination of Rmd Notebooks to conduct exploratory data analysis (EDA) with your part of the world
4. Using Pre-written Code to Generate Maps in Layers
5. 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. Make sure to relate what you have found to the Do Maps Lie video and the reading by Kitchin & Lauriault.

Step 1: Downloading or querying data:

Step 2: Choosing the features of interest to you:

Step 3: Filtering for these Features:

Step 4: Using Pre-written Code to Generate Maps in Layers

Step 5: Written Synthesis:

Guiding Questions

As you write, consider (but don’t feel obligated to answer) all of these questions:

• Background & Expectations: What did you know about your location before beginning analysis? What hypotheses did you have about the kinds of geographies contained in the dataset?

• Computational Insights: What interesting patterns emerged?

• Trends & Surprises: Were there unexpected findings? How do your results compare to your initial hypotheses?

• Methodological Questions:

• Scope & Scale:

• Transferability: How might you use this workflow 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 tools like Markdown Live Preview and Hemingway App to refine your prose for clarity and legibility. Alternatively you can use the preview function in Visual Studio Code itself. Keep the F-shape principle for web writing in mind—readers scan top-to-bottom and left-to-right, so structure your argument visibly.

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).

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!

Assignment 2 invites you to compare the classification of the Stylo package in R and of a TF-IDF (term frequency inverse document frequency) approach with a corpus of texts by science fiction writers from Project Gutenberg. This assignment builds on concepts and tools we’ve discussed in class—particularly exploratory data analysis (EDA) using the two methods. It asks you to compare the two approaches and to synthesize your findings in a web-facing written essay with supporting visualizations.

• Format: Individual or pairs (maximum 2 people)
• Length: Approximately 1500-2000 words (about an 8- to 10-minute read), plus visuals
• Due Date: Tuesday, 28 April 2026, 11:59pm

The Corpus

A corpus of 18 texts by science fiction writers has been curated for you. These texts were all taken from Project Gutenberg.

Bonus: If you would like to try something a little extra, keep these 18 texts, but use your favorite LLM to generate a few short novellas (2000 words minimum) in the style of one of the authors on the list. Add these to the corpus and include them in your assets folder in Github for me to see. Include the prompt you used and the conversation with the LLM in the post of your assignment.

Five Core Elements

This assignment has five core components:

1. Corpus Research: You will be provided with a corpus and will be asked to work only with those texts. No corpus choice is required. That corpus will be in a folder in our class drive marked “Assignment 2 corpus.” Nonetheless, you will need to do some research into the authors and the texts included. Three convenient places to do research are Project Gutenberg (summaries), Wikipedia and the Internet Speculative Fiction Database. The latter is specifically helpful for the crowd-created tags it contains for individual works of fiction. As in Assignment 1, research into the texts and contexts will be essential for your discussion. We took some collective notes about the authors in class. They can be found in Drive.

2. Exploratory Analysis: You will do exploratory research using the two methods comparing the way that the two methods treat the same texts. You will be asked to use a notebook in posit.cloud in order to carry out the research with Stylo. The results of the TF-IDF will be pre-computed for you across a variety of parameters (100, 300, 500, 2000, 3000 most frequent words).

3. 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. Make sure that your visuals contribute to the telling of your story.

4. Integration of Course Materials: Return to the article by Underwood, “The Dangers of Distant Reading.” Refer to it in your assignment where appropriate.

Referencing other readings or resources (podcasts, articles) from this course in your essay is optional. You may also draw on external sources as appropriate.

Instructions for the RMarkdown notebook (posit.cloud) and TF IDF visualization

RMarkdown

In our class posit.cloud space you will find a notebook entitled “Stylometry with Science Fiction Authors from PG”. In it, there are steps to create some unsupervised clustering of the 18+ texts of the corpus using the stylo package in R. NB: If you are using the LLM-generated texts in addition, you will need to upload those texts into the corpus folder. If you add more than one at a time, you will need to compress them into a zip file.

You should try the Cluster Analysis (CA) across different numbers of MFWs, as well as bootstrap consensus tree (BCT). Include the visual that helps you explain your findings the best. Pay attention also to the wordlist.txt file that is created.

TF-IDF

I have pre-computed the TF-IDF for you and these are found in Drive. There are five folders in Drive in the Assignment 2 folder corresponding to the 100, 300, 500, 2000, 3000 MFWs. In case you want to run that code yourself (advanced step) the corpus is only included in the folder for 100 MFW.

When you download the folders, click on either light.html or dark.html to view the visualization in the browser. You can use the interactive buttons to look at different information from the data. You do not need to rerun the code. You should have everything you need in the interactive visuals of the analyses at 100, 300, 500, 2000, 3000 MFWs.

Guiding Questions

As you write up your findings, consider (but don’t feel obligated to answer) all of these questions:

Background & Expectations of the Corpus:

• 
  - What did you know about science fiction / speculative fiction before working with this corpus?
• 
  - Are all of the texts in this corpus the same length
• 
  - Do any of the writers write in very different ways
• 
  - Did you close-read any of the texts included in the corpus?
• 
  - What kind of thematic difference exists in the corpus?

Methodological Expectations:

• 
  - How can you compare the different methods Stylo for hierarchical cluster and TF-IDF PCA clustering for what they purport to analyze?
• 
  - What do you think about the idea of function words or most distinctive words being the basis for distant reading?
• 
  - What kinds of words are likely driving each method’s results?

Computational Insights:

• 
  - What did the computational analysis of the corpus teach you about science fiction / speculative fiction that you did not already know?
• 
  - How does the wordlist generated by Stylo compare to the loadings that you can visualize in the TF-IDF PCA visualization?
• 
  - Would you expect the loadings to be the same across different numbers of MFWs?
• 
  - Were there words specific to the loadings or the wordlist from stylo?
• 
  - Was the gender of the writers linked to any stable kind of clustering? What about theme of the book?
• 
  - Identify one case where the two methods disagree, even if slightly. What might explains this difference?

Comparative Insights:

• 
  - How do the clusters that you get in hierarchical analysis compare to the proximity of the points you get in the TF-IDF PCA visualization?
• 
  - What is similar about them? What is different?
• 
  - Typically one would say that Stylo is good for detecting authorship and similarity in style of writing and TF-IDF is good for detecting content similarity? Did you find this to be true?
• 
  - If any of the texts were co-authored, did this make a difference for the clustering?

Trends & Surprises:

• 
  - Were there any surprises that you found when working with both methods?
• 
  - Did your analysis want to make you want to read any of these novels more deeply?
• 
  - If you took on the bonus part of the assignment to generate examples in the style of “Author X” what happened to the clustering?
• 
  - What did the LLM understand by style?

Thought Questions:

• 
  - Are style and content truly separable?
• 
  - By understanding science fiction computationally, do you think this will lead to AI-generated new forms of scifi?
• 
  - What did you think about these novels mostly from the 1950s?
• 
  - How do you think they spoke to a post-WW2, Cold War American readership?
• 
  - What kinds of new literacies are required when we work with code to “read like a computer”?

Assessment

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

Marking your assignment as done

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!

Response 1 is an open-ended short essay style assignment that invites you to reflect on the importance of context for computational text analysis and the potential pitfalls of visualization-based interpretation for the corpus you chose. It asks you to use the visualizations that you generated in both R and Voyant Tools.

• Format: Individual (maximum 1 person)
• Length: Approximately 750 words, plus visuals (screenshots from LLM chat, prompts)
• Due Date: Sunday, 15 March 2026, 11:59pm

Three Main Elements

This response has three core components:

1. Going back to your visuals: For the purposes of Assignment 1, you generated a few visuals, some of which you may not have included in the final draft. You will need to assemble these. You can also use any of the visuals that we generated in the notebooks in class or that appeared in one of the readings.
2. Interacting with the LLM(s) of your choice: Use any LLM of your choice and in any language of your choice. Gemini is an obvious choice, since we have an institutional subscription, but you can also try one you have never used before (say, Mistral Le Chat, Perplexity AI, JAIS or Claude Anthropic).
3. Thinking and Interpreting the Results: Include selective prompts you used in asking about the visuals. Otherwise, you can structure the short response however you like.

Reference at least one (1) other readings or resources (podcasts, articles) from this course in your essay, particularly on the importance of context or visual communication. You may also draw on external sources as appropriate. Be sure to cite what you use, including LLMs. This additional one about misleading visualization might also be interesting.

Steps:

Give the visuals to the LLM of your choice, starting with no context. Prompt the LLM to explain the visuals, then interpret the visuals. Iteratively ask the LLMs more information, collect that information. Discuss it.

Guiding Questions

As you write, consider (but don’t feel obligated to answer) all of these questions:

• Do visuals speak on their own?
• If you looked at visuals created by a fellow student in their paper or lab report, how would you make sense of it?
• Without context, what does an LLM say about your visuals?
• What kind of prompts or added information help obtain a more meaningful result?
• Are there literacies or cognitive contexts that are required for visual communication to be successful?
• What kind of context is required for a visual to be understood by someone else? or a non-human?
• What makes any particular visual one worth including in writing?
• How do we contextualize our arguments by explaining visuals?
• Could you imagine using this process to make your writing with visuals better?

Assessment

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

Tips for Success

Writing: Use tools like Markdown Live Preview and Hemingway App to refine your prose for clarity and legibility. Keep the F-shape principle for web writing in mind—readers scan top-to-bottom and left-to-right, so structure your argument visibly.

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 response!

I have curated here a number of possible oral exam questions that would be relevant for the near completion of the term in this class.

Format of the Oral Exam

The oral exams will cover readings, material covered in your assignments and any material more generally in the class.

The oral exam will take place in person in my office. Everyone has signed up for a time to have their exam. It will last a maximum of 20 minutes (expect a little less). There are 10 minutes between each of the oral exams of downtime for me. Please be on time, but don’t come so early so that you wait outside my office. If late, you will only have the remainder of the time left for the meeting.

The most important part of the Oral Exam is not all the knowledge you have memorized, but that it is a “student-driven” conversation. I will expect for you to keep the conversation going. I will ask follow up questions, time permitted.

Rationale behind having an Oral Exam

• to put the course skills in a larger context
• to give students an opportunity to synthesize course materials periodically
• to use terms from the course correctly in academic conversation
• to see if you are able to revise or deepen an answer when prompted to go beyond an initial answer
• to demonstrate metacognitive and reflective thinking in an interdisciplinary context (“I know this, but I’m not sure about…”)
• to demonstrate awareness of course interests outside of the class, in a spoken register

Level 1 : Recall: Terms from the Class

• including, but not limited to, distant reading, close reading, surface reading, computational thinking, static sites, distant coding, Markdown, GitHub, Voyant Tools, wordclouds, data visualization, text editor, corpus, tidyverse, stopwords, R package

Sample question: “Tell me in your own words about the concept of X.”

Level 2: Sample Questions about the Course Materials

• Which reading was your favorite reading?
• Which one was your least favorite?
• What’s one idea from the readings you found intriguing? Why?
• What’s one idea from the readings you disagree with? Why?
• Which reading made the greatest impression on you? Why?
• Which genre of material do you like the best (podcast, essay, digital project)?
• What new technical skills have you been exposed to?
• Which tools have been the most helpful? which is the least helpful?
• Which tools do you imagine you will use after the course?
• Which element of the tools presented in the course are challenging to understand?

NB: I will have a ‘question bank’ and so they will not be exactly the ones listed above.

LEVEL 3: Questions about Your Assignment 1

• How did you choose the texts that you worked with?
• How did your assignment change as you worked on it?
• What did you learn about the topic of your assignment that you didn’t know before?
• What would be different if you had chosen more texts or fewer?
• If you had had more time for your assignment what would you have done differently?

• NB: I will have a ‘question bank’ and so they will not be exactly the ones listed above.

LEVEL 4: Questions for Reflecting on the Class more Generally

• What are you learning from this class that you have never been exposed to before?
• What are you not sure about in the class that you would like to dive deeper into?
• How do the materials of the class relate to your experiences?
• What subject of the class have you told your friends about?

NB: I will have a ‘question bank’ and so they will not be exactly the ones listed above.

In the time between the oral exams I will take notes and score the rubric. Once all are done, I will release the grades.

Good luck on your oral exam!

Assignment 1 invites you to apply computational text analysis methods to a corpus of texts you select. This assignment builds on concepts and tools we’ve discussed in class—particularly exploratory data analysis (EDA) in Voyant Tools and the two notebooks in posit.cloud-and asks you to choose a subset of either the Harry Potter and Harry Potter fan fiction corpus or the Colonial South Asian Corpus and to synthesize your findings in a web-facing written essay with supporting visualizations.

• Format: Individual or pairs (maximum 2 people)
• Length: Approximately 1500 words (about an 8-minute read), plus visuals
• Due Date: Sunday, 15 March 2026, 11:59pm

Three Main Elements

This assignment has three core components:

1. Corpus Selection: Choose three or more texts that you’d like to work with. This choice will require a bit of research.
2. Exploratory Analysis: Use a combination of Voyant Tools and the Rmd Notebooks to conduct exploratory data analysis (EDA) with your corpus
3. 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. Make sure that one of your Voyant Tools visualization is a live widget embedded in your post.

Step 1: Select Your Corpus

You have several options for corpus selection. Choose one approach below, or consult with your instructor if you’d like to create a custom corpus.

Choose five or more texts from either corpus that you’d like to work with.

• If you choose Harry Potter, choose two or more texts by Rowling and three or more from the fandom.

• If you choose the Colonial South Asian corpus, choose five or more of the texts.

Step 2: Research Your Texts

Before you begin analysis, research the texts themselves: Who are the authors? What is the publication context? What are the general themes and contents? You will want to do background research using something like Wikipedia or other reliable web sources. This research may actually inform your choice of corpus.

By becoming familiar with your texts, you’ll be able to:

• Justify your selection meaningfully
• Contextualize your findings rather than studying the corpus in isolation
• Make connections between distant reading insights and close reading knowledge
• Recognize what makes your corpus interesting

This contextual knowledge is essential for meaningful analysis.

Step 3: Conduct Your Analysis

You must use both of the following methods.

• Voyant Tools (https://voyant-tools.org/): A web-based suite for exploratory text analysis. Provides visualizations like word clouds, trend graphs, and concordances. Works well for smaller corpora (a handful of texts to a dozen or so).

• RMarkdown notebooks in posit.cloud: Use computational analysis to create word clouds or heatmaps using custom words.

Other tools are acceptable if they serve your research question. Please justify your use of them.

Step 4: Build a Collection of Visualizations

Create at least two (2) screenshots showing the results of your exploratory analysis from Voyant and a selection of screenshots from ggplot in R. These might include:

• Word frequency charts or tables
• Wordcloud visualizations
• Trend graphs showing word usage over time or across texts
• Concordance results
• etc.

Ensure each screenshot well chosen to illustrate a point and that each is clearly labeled and contextualized.

Step 5: Include an Interactive Visualization

Embed at least one interactive iframe from Voyant Tools in your essay. This allows readers to explore the data themselves. You can obtain an iframe by:

1. Running your analysis in Voyant Tools
2. Going to the Export tab
3. Copying the HTML snippet

Sample iframe:

<iframe style='width: 444px; height: 408px;' src='https://voyant-tools.org/tool/Cirrus/?corpus=8d8c7ce89087801d676ff4f77d5391fc'></iframe>

and it looks like this:

The size is adjustable using the width and height values in the iframe.

Step 6: Integrate Course Materials

Read the chapter “The Risks of Distant Reading” (pp 143-169) from Ted Underwood’s Distant Horizons, available as an e-book. Refer to Underwood’s chapter in your assignment where appropriate.

Reference at least two (2) other readings or resources (podcasts, articles) from this course in your essay. You may also draw on external sources as appropriate. Be sure to cite what you use include LLMs.

Guiding Questions

As you write, consider (but don’t feel obligated to answer) all of 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?

• Comparative Insights: What did Voyant Tools allow you to do that the Rmd Notebooks did not? How was working with the two methods different? similar?

• 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: If you ran your analysis between Voyant and Rmd Notebooks, did you get consistent results? Why or why not? How do different visualization methods 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)?

• Scope & Scale: How limiting (or enabling) was the constraint of comparing five or more texts? What would you analyze differently with more or fewer texts?

• Transferability: How might you use this workflow 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 tools like Markdown Live Preview and Hemingway App to refine your prose for clarity and legibility. Keep the F-shape principle for web writing in mind—readers scan top-to-bottom and left-to-right, so structure your argument visibly.

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!

Static sites are web pages that are delivered directly to the user’s browser exactly as they are stored. Unlike dynamic sites that generate content on-the-fly using databases and server-side processing, static sites consist of fixed HTML, CSS, and JavaScript files. This simplicity offers numerous advantages for digital humanities and sustainable web practices.

This course site is an example of a very simple static site built in Jekyll in Github Pages.

Here are some customized ones:

• The Programming Historian - also Jekyll and in Github.
• Stanford Literary Lab - they explain why they adopted a static site here
• Models Of and For - A curated directory of projects using a static web approach (Models Of), templates / frameworks for creating new projects (Models For), and helpful utilities for Lib-Static development (Tools).
• Collection Builder - an open source framework for creating digital collection and exhibit websites that are driven by metadata and powered by modern static web technology – templates that work from Google Sheets!
• Jekyll Themes for the Academic

Advantages of Static Sites

Speed: Static files load quickly since there are no server-side processing or database queries.

Cybersecurity: They have fewer moving parts and no dynamic backend processing, so static sites have fewer vulnerabilities.

Simplicity: Static sites are straightforward to understand, maintain, and deploy. No complex server configurations or database management required.

Version Control: Because static sites are typically .txt (plain text) files (often Markdown), they integrate with version control systems, enabling collaborative development. Both of the examples above are collaboratively managed.

Sustainability: Minimal server requirements mean lower resource consumption for more sustainable digital humanities projects.

Inexpensive: Hosting static sites is inexpensive, with many platforms offering free hosting options like GitHub Pages. What is required is some basic know how in versioning systems and Markdown.

Static Sites and GitHub Pages

GitHub Pages is a hosting service that makes it easy to publish static sites directly from a GitHub repository. You can

• Write content in Markdown
• Version your entire site in VSCode
• Deploy changes with a simple push in VSCode or Github Desktop (for an added sanity check)
• Use themes for your site’s appearance, and when you grow more confident, pass on to customization
• Maintain a complete history of all changes

Elements required:

1. A GitHub account
2. A text editor (we will use VS Code, etc.)
3. Git and GitHub Desktop installed
4. Familiarity with Markdown

Sustainability, Static Sites and Digital Humanities

As Wikle and Williamson argue in their Code4Lib Journal article, static web methodology represents a shift in how digital humanities practitioners approach infrastructure.

This philosophy aligns with principles of minimal computing in the digital humanities—asking what is truly necessary when developing digital scholarship under real-world constraints.

To read:
-Static Web Methodology as a Sustainable Approach to Digital Humanities Projects by Olivia Wikle and Evan Peter Williamson (Code4Lib Journal, 2025). This article provides insight into why static web approaches are gaining traction in academic and digital humanities contexts, over web publishing methods such as WordPress or Drupal.
-The Questions of Minimal Computing by Roopika Risam and Alex Gil (DHQ, 2022). The introduction to a special issue of DHQ frames “minimal computing” as a way of thinking about digital humanities that prioritizes necessary and sufficient technologies under real-world constraints, using four questions (what is needed, what is available, what must be prioritized, and what can be sacrificed) to foreground practices that resist equating innovation with scale or resource intensity.

Exploring the Components of the Minimal Mistakes Template

The Minimal Mistakes Jekyll theme, which powers this course site, is built from several key components:

• Layout Files (_layouts/): HTML templates that define the structure of different page types (default, post, archive, etc.)
• Include Files (_includes/): Reusable HTML snippets (navigation, footer, header) that are embedded into layouts to avoid repetition
• Assets (assets/): Stylesheets (CSS), JavaScript files, and images that control the site’s appearance and functionality
• Configuration (_config.yml): The main settings file controlling site title, theme, plugins, and other global behaviors
• Content (_posts/, _pages/): Your actual content files written in Markdown that Jekyll processes into HTML pages
• Data Files (_data/): YAML or JSON files containing structured information (like navigation menus) that templates can access and display

When you start with Minimal Mistakes, these components work together without issue. If you change things too quickly in the beginning you can break the site. As you gain confidence, however, you can customize individual pieces—changing colors in the CSS, modifying navigation structure in _data/navigation.yml, or adjusting layouts to create unique page designs—all without needing to rebuild the entire system.

Let’s start by looking at a few elements _data/navigation.yml, _pages, _posts and _config.yml

Today in class one of the things we will be doing is to beginning to create your own site at GitHub Pages in which you will do your coursework. Creating such a site will require us to set up GitHub, GitHub Desktop and a text editor, to learn and practice some Markdown and to get comfortable with editing on our own machine and then push slowly our materials to the web.

Knowing how to create such a “static site” is an important skill in minimal and sustainable digital arts and humanities. It is also the place you will author and submit your course materials.

This course site is created in GitHub Pages in three ways, using an imported repository, then forking the Minimal Mistakes theme, then from scratch. When you gain more confidence you will be able to change to other themes and customize to your own liking.

GitHub and GitHub Pages Terminology

Before we begin, here are some new terms you’ll encounter when working with GitHub:

STEP 1:

1. Make sure you have a Github account in which there is not already a repository {yourusername}.github.io. If you have this one set up already, you have a few options:

• you can delete the repository if you do not want it anymore and start afresh.
• you can create an empty repository named, for example RLAC, and then install the template inside of it.
• you can create a new Github account for the purposes of this course.

These instructions will guide you through the second option. If this is your first time working with GitHub, you don’t have to do anything special.

NB: Your account and your page does not need to reveal your identity in any way. Feel free to consider this account a “burner” account for the purposes of this semester. Once you have gotten your grade, feel free to delete it or to repurpose the Markdown files for another site. If you have already created a GitHub account using your name or something identifiable, feel free to create another now.

Github may require you to have multifactor authentication (2FA). We will not cover that here. It works somewhat like DUO used at NYU.

1. Make sure you have downloaded a text editor of your choice. This lab write up will explain with Visual Studio Code. Others (such as Sublime Text) are possible, but not explained here.

2. Make sure you have downloaded Github Desktop. In the beginning, we will edit in VSCode and save (ie, make our commits to GitHub using GitHub Desktop), but you will learn that you can also commit directly from VSCode. If the class feels comfortable with that second step, we will transition to it later. If you already know how, don’t worry about GitHub Desktop.

STEP 2:

Importing a repository, or “Forking a repo.”

Detailed general instructions for forking can be found here

1. Once you have completed 1 and 2 of Step 1, you can proceed with making a duplicate of the template we will be using. We do this by navigating to the main page of an existing GitHub page of minimal-mistakes and will be using their starter template: https://github.com/mmistakes/mm-github-pages-starter

2. With your GitHub account open, navigate to this page, above the file list, click Use This Template (If you do not see it, you may have to go to Settings to click it on). Select create a new repository. This will take what is at minimal-mistakes and bring it over into your account. Duplicate. Copy. Move over to your GitHub account. This is all forking.

3. On the page create a new repository you need to give the repository a name. You should see under general your username. Next to it you can choose a repository name (for example, rlacs26). Give it a description Make sure you set the repository to public and then click on the green button create repository.

4. Once the repository has been created you need to activate the “build and deploy” function for that page. Go to Settings > Pages and select the master branch and save. You can tell that something is working in the repository you just create when you go back and you see the brown dot. A green check mark means the process is done.

Note that the repo we have forked is a simplified version of minimal-mistakes. If you want to tap into all the options of the full version, you will have to download the full theme and do more work!

We will use the starter website (and customize it) for our work in CDAD-UH 1024Q, including the creation of both pages and posts.

STEP 3:

Connecting the cloud-based Github to your own machine.

1. Cloning this repository with Github Desktop

Open GitHub Desktop and log into it with your credentials from your GitHub account. Setting > Accounts.

In Github Desktop make sure you are in the current repository {username}.github.io. Then, go to File > Clone Repository or with the abovementioned repository, click on the add button and pull down for clone repository.

It may come up automatically, alternatively you can select URL, paste that URL into the repository URL blank and click clone.

When done, you should be able to see it in the current repository tab at left in Github Desktop.

1. Editing the repository on your own machine.

Now that you have a copy of the repository on your laptop you can edit it there (even offline) and then when you are ready, “push” the changes to the web.

When you edit and save (and sometime enter a commit message), you will see a blue button to transfer these changes to the web.

NB: You can do some editing in the GitHub web interface itself, but I recommend editing in Github Desktop with your text editor, and certainly not mixing the web-based interface and the text editor, since you end up with a versioning nightmare. If you do make a change in the GitHub web interface, make sure that you fetch the origin.

Let’s make some changes in the Sublime Text and push them.

1. Use the button in Github Desktop to open in external editor (choosing Visual Studio Code), we can now move on to editing any of the pages.

Let’s make some changes in VSCode and push them.

Remember that every time you push to the web, the compiler works to make your page’s updates. Be patient and look for the green arrow which indicates that your site has been rebuilt with the changes you made. If you see a brown dot next to the last commit message in the repository holding the site, the compiler has not finished. Go have some tea or a quick walk–it will be done soon.

The areas in the forked repository of the minimal-mistakes template we will want to look at next include:

• navigation.yml
• config.yml
• /assets/images/

Enjoy!

Follow up posts:

• Committing directly in VSCode – coming soon
• Connecting your VSCode to CoPilot and Customizing your GitHub page – coming soon

Assignment 3 invites you to compare different abstraction representations of our corpus of about 1000 texts by science fiction writers from Project Gutenberg. This assignment builds on concepts and tools we’ve discussed in class—particularly exploratory data analysis (EDA) using other methods. It asks you to compare differently trained models and to synthesize your findings in a web-facing written essay with supporting evidence.

• Format: Individual or pairs (maximum 2 people)
• Length: Approximately 1500-2000 words (about an 8- to 10-minute read), plus visuals
• Due Date: Tuesday, 12 May 2026, 11:59pm

This assignment is optional. You can receive up to five (5) points of extra credit.

The Corpus

A corpus of 1000 texts by science fiction writers has been scraped from Project Gutenberg and the front and end matter have been removed. Those 1000 texts are the same we used for topic models. They are available in Drive. They have not been included in posit.cloud to save space.

Five Core Elements

This assignment has five core components:

1. Corpus Research: It will be next to impossible to do research on the entire corpus since there are so many authors and texts, but you will want to come up with some different “angles” that are of interest to you in science fiction. These will help you to define certain starting words. If you are interested in looking at specifics of clusters or analogies, you can use the free and open source tool AntConc (which allows for searches like Voyant, but working with 1000 files). This will be useful to “trace back” to individual texts that use specific words.

2. Exploratory Analysis: You will do exploratory research using the multiple methods outlined in a notebook in posit.cloud (Word Vectors and SciFi authors) in order to carry out your research with. You do not have to train the word vector models as they have been pretrained. Here are the models contained in posit.cloud:

The notebook proposes a number of different kinds of analysis: clustering, finding words closest to a single term or closest to multiple terms, semantic “subtraction” (finding words closest to the difference between two terms), analogies, as well as other forms of vector “math” (Vector Averaging, Orthogonal Projection, Centroid Comparison). Given that this is an extra credit assignment, you can do as many of these as you would like.

1. 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. Make sure that your visuals contribute to the telling of your story. In this assignment, visuals will likely be lists of words in tables.

2. Integration of Course Materials: Optionally you can return to the article by Underwood, “The Dangers of Distant Reading.” Refer to it in your assignment where appropriate. Referencing other readings or resources (podcasts, articles) from this course in your essay is optional. You may also draw on external sources as appropriate.

Guiding Questions

As you write up your findings, consider (but don’t feel obligated to answer) all of these questions:

Background & Expectations of the Corpus:

• What time periods are represented in the corpus (think about the topic modeling and time notebook), and how might this affect language and themes?
• What do you expect to be the dominant themes in scifi literature (e.g., space, technology, dystopia)?
• Which words do you expect to appear in similar contexts (e.g., “ship”, “planet”, “crew”)?
• What conceptual differences might exist between early vs later sci-fi texts?
• Do you expect differences between human-centered vs machine-centered vocabulary?
• What biases might exist in a corpus drawn from public domain texts?

Computational Insights:

• What do you think it mean for the word vector model to learn meaning from context?
• Pick some clusters of words from some of the models and identify what they are about? What is the relationship to science fiction?
• Which words are closest to a target word? Do these close words match your expectations?
• If you make the same queries across different iterations (iter) how stable are the results?
• What role does negative sampling play in distinguishing words?
• Does the window size affect what counts as context?
• What differences do you see with higher vs lower vector dimensions?
• What kinds of relationships does the model capture most clearly?
• How much of your human mind do you feel you bring to this kind of analysis?
• If we had included the translations of Wells’ novels in the model training, what do you think would have happened?

Trends & Surprises:

• What word relationships surprised you the most?
• Are there strange or unintuitive nearest neighbors?
• Do certain sci-fi terms dominate the semantic space?
• Did you ever find yourself in a “corner” of semantic space where the relationships were not clear? or seemed very specific to some texts?
• When you switched models, did you find the “best for” explanation above in the column to hold true?

Thought Questions:

• What does it mean for a machine to learn meaning without definitions?
• How does word2vec’s “reading” differ from human interpretation?
• What kinds of meaning does the model capture well? least well?
• How does changing parameters change what the computer “sees”?
• What kind of evidence would you call vector relationships?
• Is word2vec analysis an interesting method to you? Compare it to others we have looked at.

Assessment

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

Marking your assignment as done

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!

THIS IS A DRAFT POST

GREEN TIP: If you would like to be more sustainable about your use of computing resources, you can test your markdown in a live viewer such as this one before sending it to the compiler.

THIS IS A DRAFT POST

GREEN TIP: If you would like to be more sustainable about your use of computing resources, you can test your markdown in a live viewer such as this one before sending it to the compiler.