Over the past few years I’ve become interested in better understanding how my own discipline works. As someone whose work has changed considerably over the past decade, it’s probably a predictable response. In one sense, it is about asking, How do I fit in? On…
In a new work out, I have teamed-up with my collaborator Stephania DeGaetano-Ortlieb to try to model what we call “the scientization of literary study.” The study of literature has historically been seen as a scholarly practice that is distinct from the natural sciences. Literary…
As part of BookNet Canada‘s on-going webinar series, I recently held a webinar on using data analytics to “debias” books. A lot of discussion has begun in publishing with respect to increasing the diversity of publishers’ lists. The issue is often seen as one that…
A new student collaboration is out. It’s called “Streaming Bias: studying music curation on Spotify” and represents the lab’s first attempt at studying online music content. Lin and LeBrun did an amazing job in conceptualizing the project and working tirelessly from data collection to analysis…
If you’re like me, you’re probably feeling overwhelmed right now. Continuing to do academic research in this climate is very hard, to say the least. But if you’re like me, you’ve probably also asked yourself more than a few times, is there something I can…
Our latest lab collaboration was created by McGill undergraduate Brian Powell. As a college basketball fan, he was interested in whether the high levels of institutional inequality within academic publishing and hiring that we have been seeing would also hold true within college athletics. Did…
This March I will be giving a talk at BookNet Canada’s TechForum. The topic is “debiasing” books and I’ve posted a short blog post to get the conversation started. Discussions of bias, and cultural bias in particular, have been much in the air lately. A…
For the latest instalment of the Fish and the Painting I have been wrestling with topic modeling. I say wrestling not because the coding is hard but because the analytical value of topic modeling is so hard. Topic modeling has become a kind of go-to…