Equitable Information Access and Librarianship Praxis: Let’s Get Critical

Today’s Read:
Lauren Smith & Michael Hanson
Communities of Praxis: Transforming Access to Information for Equity
The Serials Librarian, v. 76, nos. 1-4, pp. 42-49
DOI: 10.1080/0361526X.2019.1593015

I recently submitted a proposal to write a chapter on Critical Legal Studies for a new library science textbook, and it was accepted.

In the proposal, I used the Critical Legal Studies research guide I created for our library to create a framework for the future book chapter. Basically, I want to get the content of this guide into some kind of written form, since library research guides have a bit of an ephemeral quality.

While the chapter will be primarily about critical studies in law, it will also include some basic information about critical librarianship. I want to encourage future law librarians consider issues of disproportionate representation and information access in their own professional practice. Or, put another way, I hope that they will choose to incorporate praxis into their practice of librarianship.

For this reason, this recent article from The Serials Librarian caught my eye, and I decided to blog about it. The article is based on a presentation given by Lauren Smith at the 2018 NASIG (formerly the North American Serials Interest Group) conference. In her talk, Smith discussed three themes that are necessary to “democratize” information, which means making sure that all people are empowered to exercise their right to access it: power, praxis, and privilege.

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“Why do people trust algorithms?”

Veronika Alexander, Collin Blinder, and Paul J. Zak, Why Trust an Algorithm? Performance, Cognition, and Neurophysiology, Computers in Human Behavior 89 (2018) 279-288, https://doi.org/10.1016/j.chb.2018.07.026.

As a librarian, I am interested in information behavior. I am not a computer scientist or a programmer, so my knowledge about the intricacies of algorithms is minimal. But I try to inform myself where I can.

I found this study to be helpful because it addressed how trust in algorithms affects behavior. The authors conducted a study in which they measured participants’ neurophysiological responses to gain a better understanding of people’s perception of the trustworthiness of algorithms. Their hypothesis? “Higher algorithm accuracy and greater prior use by others would result in higher algorithm adoption … (and) social proof would influence adoption more than algorithm accuracy information.”

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