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Welcome to .txtLAB, a laboratory for cultural analytics at McGill University directed by Andrew Piper. We explore the use of computational and quantitative approaches towards understanding literature and culture in both the past and present. Our aim is to engage in critical and creative uses of the tools of network science, machine learning, or image processing to think about language, literature, and culture at both large and small scale.
Author Archive
Are novels getting easier to read?

Are novels getting easier to read?

I’ve been experimenting with using readability metrics lately. They offer a very straightforward way of measuring textual difficulty, usually consisting of some ratio of sentence and word length. They date back to the work of Rudolf Flesch, who developed the “Flesch Reading Ease” metric. Today, there are over 30 such measures. Flesch was a Viennese...
An Open Letter to the MLA

An Open Letter to the MLA

Dear Prof. Taylor, I am writing to you as a member of the MLA who has concerns about the practices and policies relating to the society’s data and its impact on research. This is an issue that effects many scholarly organizations. For this reason I have chosen to write an open letter. The MLA has...
The Legibility Project: Reversing the dark economy of academic labor

The Legibility Project: Reversing the dark economy of academic labor

Here is an example of the kind of registry I am thinking of, using my own activity as a starting point. On-going duties include: Undergraduate Advisor European Studies Minor, Editor Cultural Analytics, Board Member Centre for Social and Cultural Data Science Over the years I have become aware that a significant portion of my time is spent...
1000 Words

1000 Words

Lab member Fedor Karmanov has created a beautiful new project that combines machine vision, machine learning, and poetry. It is called “1,000 Words,” and takes the self-portraits of Van Gogh and generates poems based on the colours and items in the portrait. The poems consist of 10 lines randomly drawn from an archive of about...
The Danger of the Single Story - Why Quantity Matters

The Danger of the Single Story – Why Quantity Matters

I listened to a beautiful podcast the other day by Chimamanda Ngozi Adichie on “the danger of the single story.” Her point was that when we only tell one kind of story about a person or a place we cheapen our understanding. She began with her experience as an African writer, one who all too...
AI across the Generations

AI across the Generations

I gave a talk today with Paul Yachnin to the McGill Community for Lifelong Learning on “Conscientious AI.” The idea for the event was to give the audience some understanding of how machine learning works and what you might do with it. We then asked the tables to brainstorm ideas about what kinds of AI...
On Prestige Bias in the Chronicle of Higher Ed

On Prestige Bias in the Chronicle of Higher Ed

The Chronicle of Higher Education ran a version of our essay on the concentration of institutional prestige as its cover story this week. In it we expand our reflections about how to change the current system. The essay is based on our original piece that appeared in Critical Inquiry. Here is an excerpt from the...
The Prestige Trap

The Prestige Trap

I am pleased to announce the publication of a new piece out with Chad Wellmon in Critical Inquiry entitled, “Publication, Power, and Patronage: On Inequality and Academic Publishing.” In it we discuss the concentration of a few elite institutions within a sample of four humanities journals stretching back over forty years. Our goal is to...
Think Small: On Literary Modelling

Think Small: On Literary Modelling

This is the name of a new piece I have out in PMLA in a section called “Franco Moretti’s Distant Reading.” The first point I try to make is that calling it “Moretti’s Distant Reading” is indicative of literary studies’ continued penchant for great men. It is ironic, or telling, that even in an issue...
Why are non-data driven representations of data-driven research in the humanities so bad?

Why are non-data driven representations of data-driven research in the humanities so bad?

One of the more frustrating aspects of working in data-driven research today is the representation of such research by people who do not use data. Why? Because it is not subject to the same rules of evidence. If you don’t like data, it turns out you can say whatever you want about people who do...
Data Visualization and Reading - An Interview

Data Visualization and Reading – An Interview

Mark Algee-Hewitt and I recently took part in an interview with Elyse Graham for a special issue of English Studies on “Data Visualization and the Humanities.” You can read her introduction here and our interview here. We touch on a bunch of topics about visualization: like whether data visualization is exclusively exploratory, whether the humanities...
Cultural Advocacy Internship - "Gender Bias in Book Reviews"

Cultural Advocacy Internship – “Gender Bias in Book Reviews”

We are excited to announce the 2017-2018 Internship in Cultural Advocacy, focusing on gender bias in book reviews. The internship will address how women are both mis-represented and under-represented in the public discourse of book reviewing. Book reviews represent a significant cultural outlet that bestows authority, but as our lab’s new website called “Just Review” shows, there are a...