By Kaitlyn Hawkins, with Rui Liu, Max Liborion, and Molly Rivers

When citational politics was first brought up as a potential topic for a project for the CLEAR lab, I can’t say I knew much–or cared much–about the topic. I didn’t know much about “citational politics”, other than the fact that being cited was a good thing that would help secure careers in academia. Much to my disappointment at the time, citational politics became the lab’s chosen project.

Citational politics, or the politics of citations, refers to the politics associated with the process of citing sources in academic writing – with the most commonly cited sources being peer reviewed, academic sources written by white men while  other groups, like women, Indigenous peoples, Black people, people of color, queer folks, and holders of local knowledge, among others, are underrepresented to varying degrees (Mott & Cockayne, 2017). Our aim for the Citational Politics Project is to determine how to begin citing beyond the normal scope of sources and how to navigate doing so when academic expectations, norms, and infrastructure make this challenging. 

To introduce us to our new lab project on citational politics, CLEAR’s lab director, Dr. Liboiron, provided us with a list of readings to read before our first scheduled meeting to discuss the project (Mott & Cockayne, 2017; Ahmed, 2013; CBC Radio, 2018). Based on my limited knowledge on the politics of citations at the time, I didn’t really understand how we were going to make a project out of this. But, what I read in those assigned readings and in other pieces that those readings led me to, completely and utterly changed my entire thought process on the subject and frankly blew my mind. It was from then on that I began to learn a lot of shocking knowledge about the politics of citations. 

In Maliniak, Powers, and Walter’s study onThe gender citation gap in international relations” (2013), they examine citation and publication patterns between men and women. A key finding in their study was that, in international relations literature, “articles authored by women are cited less on average than those authored by men” and that the “gap disappears as soon as women coauthor with men” (Maliniak, Powers, and Walter, 2013: 4). Citation for scholars is important for many reasons, including securing tenure, promotions, and salary (Mott & Cockayne, 2017) so to conclude their study, Maliniak, Powers, and Walter suggest methods for reducing this gap and increasing the frequency of women being citing:

“If self-citation is a common and conventional practice, and we know it is, then women need to be encouraged to advocate for themselves and their work… faculty should also make female students aware of the benefits of coauthorship across gender lines since collaboration may be one way to increase the visibility of one’s scholarly work”

(2013: 31)

This passage really pushed me over the edge. It’s an actual ‘suggestion’ from a published paper! It isn’t a suggestion for a solution to the problem of citation politics, it’s a ‘deal with it yourself’ suggestion. It’s leaving women, people of color, Indigenous peoples, and other marginalized groups all on their own to deal with this themselves.

This topic of marginalized groups being under-cited became startling for myself as I read more on the subject, and so did the topic of the racist and sexist nature of algorithms within search engines — the steps followed by search engines to determine the significance and relevance of resources to the search topic — and how this leads to invisibilizing techniques for finding literature. I came to experience this first hand while working on our project (Mott & Cockayne, 2017; Noble, 2018).

I began looking for more women and gender minority authors to cite in our scientific research. While I was working on this, I searched for and read many different papers. At some point during all my reading and searching, I found a paper written by a Black woman from an African university that did a review of plastic pollution in aquatic environments. I had known the author’s gender, race, and geographic positionality from when I first came across the paper because part of the project concerned being aware of who we were citing and so I had gathered what I could about the author online — except her name, apparently. A couple days later, while continuing to work on citing more women and gender minorities, I went back to my records looking for the paper but discovered that I had forgotten to save it. I didn’t think this was a big deal at the time. I knew I had gone down many rabbit holes to find the paper initially, but I knew the gist of the paper, I knew the author was a Black woman from an African university, and as my memory recalled, I knew that their last name started with an ‘S’. 

I went to the university’s article database but finding this paper again wasn’t as easy as I thought it would be. I plugged in every search term I could think of. After looking over multiple pages of search results, all that showed up were articles about plastics written by white men (and a few white women). Frustrated, I then went to Google Scholar but got the same result. I knew the paper existed, I essentially wrote the key terms out in the search bar, but still I couldn’t find it. It was so infuriating. 

Eventually I did end up finding the paper, but I was still stunned at how difficult it was:

Shilla, D. (2019). Status updates on plastics pollution in aquatic environment of Tanzania: Data Availability, Current Challenges and Future Research Needs. Tanzania Journal of Science, 45(1), 101-113.

Even when I knew what the paper was about and had that knowledge to guide my search, the deprioritization of marginalized authors was so deeply embedded in these algorithms that I had a very hard time finding it again. I brought up my experience with the lab during one of our project meetings and was given a reading by CLEAR’s lab director, Dr. Liboiron, about algorithms and how they function to both invisibilize groups and promote racism: Algorithms of oppression: How search engines reinforce racism by Safiya Umoja Noble.

In Algorithms of Oppression, Noble discusses the racism and sexism of algorithms within Google:

“A “glitch” in Google’s algorithm led to a number of problems through auto-tagging and facial-recognition software that was apparently intended to help people search through images more successfully. The first problem for Google was that its photo application had automatically tagged African Americans as “apes” and “animals… Google’s position is that it is not responsible for its algorithm and that problems with the results would be quickly resolved.”

(2018: 8)

Searches not only make some things apparent and link terms in racist or sexist ways, as Noble shows, but they can also make things invisible. While searching invisibilizing techniques on my own, I came across a paper on policing algorithms, “Challenging Racist Predictive Policing Algorithms under the Equal Protection Clause by Renata O’Donnell. The piece provides information on how algorithms work and how computers and the Internet in general can be “trained” to be racist based on the racist behaviours (whether knowingly or not) of the people that program and use them. In the paper, O’Donnell (2019) discusses how policing algorithms often flag minority groups as supposedly more likely to commit crimes. These groups are then disproportionately targeted and questioned by law enforcement based on nothing more than a prediction from an algorithm.

I began to relate the information I learned in these articles to the experiences I had while searching databases for scientific sources to cite. I became very aware of just how blindingly good at invisibilizing minority groups these algorithms are and was rather embarrassed that I hadn’t noticed it before. Up until this project, I had never taken the time to really look at who I was citing. The only consideration I had put into the fact that the authors were almost always white men (and some women) was that I guessed this kind of research just wasn’t being done by anyone else – when in fact it very much was, but was actively deprioritized by algorithms. Believing this was the case and not questioning it was both a mistake on my part as well as evidence that what and how we know, and from who we know, are overdetermined by racist and sexist algorithms — underscoring how these invisibilizing algorithms are working exactly as they’re made to — to reproduce dominant power dynamics.

The lack of accountability shown by those in positions of power and privilege, including my own blindspots around the racial and gendered politics of knowledge production prior to this project — being a white woman and settler– is a big part of this problem. The need for accountability and action to rectify the harm that algorithms cause extends not only to search engines but to all platforms where knowledge is produced and shared, including and especially in academia where who you cite directly adds to that body of knowledge and to the knowledge that is subsequently reproduced and expanded on (Czerniewicz, Goodlier, & Morrell, 2017; Block, 2020; Mott & Cockayne, 2017).

While outrightly changing algorithms to prevent the invisibilizing of marginalized groups is something I’m not capable of doing myself, actively finding ways around this issue, successfully citing women, Indigenous peoples, Black people, people of colour, queer folks, and holders of local knowledge, among others, in my acts of knowledge production and adding to the shared body of knowledge on citational politics, are actions that I can hold myself accountable to do. While I’m not exactly sure yet what all of these alternative approaches will be, I am committed to putting time and effort into further experimenting with methods for citing more equitably. While this is a solid start, it should be emphasized that individual acts of better citational practices and knowledge mobilization are not enough to sway the algorithms away from their current deprioritizing, invisibilizing, racist, and sexist nature and that these acts must be accompanied by mobilizations for social equity and structural change to truly lead to change.

Kaitlyn Hawkins (she/her) is a settler from Newfoundland and Labrador, the ancestral lands of the Beothuk. With a B.Sc. from Memorial University, she works with CLEAR as both a researcher and as the lab’s manager.

This piece is the second in a series of writing by members of CLEAR’s Citational Politics working group. Other posts include:
Molly Rivers, Waking up to the politics of citation
Max Liboiron, Firsting in Research

References

Ahmed, S. (2013, September 11). Making feminist points [Blog post].  Retrieved from: https://feministkilljoys.com/2013/09/11/making-feminist-points/

Block, Sharon. (2020). Erasure, Misrepresentation and Confusion: Investigating JSTOR Topics on Women’s and Race Histories. DHQ: Digital Humanities Quarterly, 14(1).

CBC Radio (2018, February 23). The politics of citation: Is the peer review process biased against Indigenous academics? CBC. Retrieved from: https://www.cbc.ca/radio/unreserved/decolonizing-the-classroom-is-there-space-for-indigenous-knowledge-in-academia-1.4544984/the-politics-of-citation-is-the-peer-review-process-biased-against-indigenous-academics-1.4547468

Czerniewicz, Laura, Goodier, Sarah, and Morrell, Robert. (2017). Southern knowledge online? Climate change research discoverability and communication practices. Information, Communication & Society 20(3): 386-405.

Maliniak, D., Powers, R., & Walter, B. F. (2013). The gender citation gap in international relations. International Organization, 67(4), 889-922.

Mott, C., & Cockayne, D. (2017). Citation matters: mobilizing the politics of citation toward a practice of ‘conscientious engagement’. Gender, Place & Culture, 24(7), 954-973.

Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. nyu Press.

O’Donnell, R. M. (2019). Challenging racist predictive policing algorithms under the equal protection clause. NYUL Rev., 94, 544.

Shilla, D. (2019). Status updates on plastics pollution in aquatic environment of Tanzania: Data Availability, Current Challenges and Future Research Needs. Tanzania Journal of Science, 45(1), 101-113.

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