Between the annual shows, with its blogs, tweets, and Facebook posts, Canada Reads allows Canadian scholars access to an enormous body of reactions to literature. At least part of the importance of this archive is its public accessibility, which makes it equally available to both specialist academic critics and students.

Here we encourage students to search the archives of Canada Reads. Try to focus on the different ways that a text has been discussed on the show by the celebrity judges, and online by non-specialist readers:

  • What kinds of assumptions do readers make about literature in their responses? What do they find valuable or noteworthy? What do they find problematic or troubling?
  • How important is pleasure in their responses to the texts?
  • Do the readers stress the pleasure or politics of the text in their responses? Are there readers who place an emphasis on both?
  • When readers make sweeping pronouncements like “This book changed my life” or “This book made me rethink Canada’s history,” what kinds of evidence do they provide?
  • How has the program evolved over the years? How do the debates change with different hosts, settings, and themes?
  • How does the presentation of the program differ between audio and video?

As you take up one or more of these questions you might also want to think about the ethics of how you will represent your findings:

  • If you use discussion posts on the CBC blogs or website how will you cite the authors who post them?
  • Is it fair to critique such posts academically?

Lastly, the evidence, while enormous, is self-selecting. Not all readers will post online, and those who do may have multiple, and at times conflicting, reasons for doing so. This creates what scholars call a sample bias. In any study, some participants, groups, or demographics of people are more likely to respond than others; therefore, the sample can skew towards results that favour those inclined to respond. Good academic researchers take sample bias into account when they extrapolate theories from data. How will you deal with sample bias questions in presenting your findings?