Accessibility and inclusion in Bayes Rules! book: Part II.
Edit: most of this post’s content is now provided as a preprint: Framework for Accessible and Inclusive Teaching Materials for Statistics and Data Science Courses
This post is the second post on a three post series on an undergraduate Bayesian statistics book that I am writing with with Alicia A. Johnson and Miles Ott called Bayes Rules! An Introduction to Bayesian Modeling with R. You may want to read the previous post to get a better understanding of the flow.
When we submitted our book proposal, this is what we had written:
Our commitment to inclusivity is reflected from the way that we deliver the material to the types of examples we choose. Mainly, we do not assume that all readers share the same experiences and cultural references.
We are not inclusion experts but we are in higher education and educating the next generation of statisticians and data scientists. We need to hold ourselves accountable. We are continuously learning on the topic. In this post I will share some of our current efforts.
What does inclusion mean for me for a textbook? I would like our readers to find people like themselves in the book. In addition to actually learning Bayesian statistics, I also want them to be included and be part of the #bayesrulesbook community. This is an important task if we want learners to become Bayesians, we need to make them feel that they belong.
Even if nobody reads our book elsewhere, we know for sure that our students will read our books. When we think of the students we teach and the names in textbooks, most textbooks do not come close to representing our students. So in our book, in addition to having people named Matt and Rachel, we also have Mohammed, Priya, Gloria, Zuofu, and Menglin. Hopefully readers from around the world will also find names that are familiar to them as well.
We are teaching Generation Z and their perception of topics on sexual orientation and gender identities is much different than our generations’. We make sure non-heterosexual identities are represented in the book as well. You are probably wondering, how on earth does a Statistics book include LGBTQIA+ individuals? Currently we have an example on dating apps (How can we teach Gen-Z and not use dating apps as an example?). We use three singular person pronouns he, she, they to refer to people in our examples.
After reading the values and metrics of Data Feminism we also started holding ourselves accountable about the people we cite. After all, we do not only want learners to have a sense of belonging and find people like themselves in our book, we also want our statistician colleagues to find people like themselves in our references. We do not have any set rules such as “50% of the authors we cite have to be women” but we also know that if we do not make an intentional attempt to cite diverse body of scholars, we will keep only learning from the scholars that have been cited over and over for decades. There are important scholars whose work we all should know and cite but there are other important works that go unnoticed.
As we do our homework to learn more we rely on networks that already exist. For instance, Mathematically Gifted and Black features many Black statisticians. We are taking the time to read about them, follow them on social media, search for their work on Google scholar, and read their work as much as we can. Another network is ASA’s Committee on Women in Statistics that runs a curatorial Twitter account. Even though it might be a minor step, as women share their work or works of other women, we are learning about them. All the coauthors of the book including me are US based.1 We mainly go to conferences in the US. We want to know the works of our international colleagues as much as we can in Bayesian statistics.
Last but not least, we want to be mindful of not only who we cite but how we cite as well. Trans scholars have so much record of their scholarship with their deadnames and publishers do not let trans scholars to change their published work to use their name. We do not have a way of knowing much about gender identities of dead scholars but for those who are alive, it is 2020 and many scholars have websites, ORCID IDs, and social media accounts. It is our responsibility to try our best to search for scholars online and find out their name and preferred gender pronoun. If Google scholar and publishers do not want to change things, we should not leave trans scholars’ well-being to the publishers and Google scholar. Their well-being will impact their contribution to science. Will it take a long time to go over each scholar in our references? Yes it will. Do I still consider this as my responsibility? Yes, I do. Also, there are guidelines on trans citation practices that I will definitely rely on.
Miles is in Massachusetts, Alicia is in Minnesota and I am in California.