In a lecture on mass media, one of my undergraduate professors posited a question: “Does media shape culture, or does culture shape media?” As we would come to learn throughout the course, the answer wasn’t either but both.

The same is true for technology. In the series foreword for “Pattern Discrimination” by Apprich, et al., the authors quote Friedrich Kittler: “Media determine our situation.” In the series of articles that follows, authors address points in the continuum between technological determinism and social constructionism, while explicating issues ranging from homophily in network science to the politics of pattern recognition.

Throughout the Text Analysis course we’ve learned about various tools and methodologies. We’ve also been encouraged to think critically about data, its collection, and use. And while some of the dystopian futures presented in the series are more imaginative, some (especially regarding pattern bias) are present, here and now, affecting and shaping our lives.

In “Queerying Homophily” part of the series, Wendy Chun states: “It is critical that we realize that the gap between prediction and reality is the space for political action and agency.” There are also other spaces, at the personal and interpersonal-level where we can contribute to how society and technology are shaped, experienced, and lived. There are decisions made on every level of data collection, manipulation, and programming. There are also decisions we make in how we interact, petition, and talk about our workspaces, communities, and experiences.

This week, we were introduced to a basic TensorFlow template for creating a predictive model for text. The immediate possibilities were exciting; however, as the articles emphasize, excitement should be tempered by thought, action by caution, and seek “unusual collaborations that both respect and challenge methods and insights, across disciplines and institutions” as stated by Chun.

As tools proliferate, how we consider and utilize tools become more and more important. But perhaps more practically, how we view reality is of equal importance–the communities, language, and environment that surround us. Chun says: “Rather than similarity as breeding connection, we need to think, with Ahmed, through the generative power of discomfort.” Productive discomfort holds the potential of creating more human and inclusive patterns.

Sara Mills defines a post-feminist text analysis as “one which is able to see that there are, within the parameters of the textual and discursive constraints, multiple interpretations of terms and discourses as a whole.” (Mills, 1998) Mills’ approach takes into account the impact of feminism and offers a more nuanced perspective compared to earlier analyses that focused on overt sexism and discrimination. Mills references Toolan’s argument that analysis should move away from “easy examples of sexism and racism” and to “subtler and hence more insidious discriminatory and exclusionary discourses that abound.”

Mills’ approach can be useful in evaluating, generating, and re-envisioning the machine-aided methods of text analysis that are becoming increasingly prevalent. In Lauren Klein’s book Data Feminism (2020), Klein offers a similar critique by comparing approaches to data by Facebook and the Make the Breast Pump Not Suck Hackathon. While Facebook expanded gender categories, their policies did little to protect and instead actively imperiled “the safety of some of the platform’s most marginalized users.” On the other hand, used reductive data categories to achieve goals of inclusion and equality. Klein’s examples demonstrate that a more nuanced approach to data and text analysis is necessary to to identify and address the subtler forms of discrimination and exclusion that can still occur.

In the field of translation, machine-assisted approaches are becoming more common. Although machine-assistance can improve efficiency and fidelity, it introduces another layer of complexity to the process of translation. Mills’ approach could aid in examining not only the product of translation but also the underlying systems and processes, including machine-assisted translation. While Taivalksoki-Shilov (2019) addresses some of the ethical concerns regarding machine-assisted translation such as the quality of translation and preservation of the author’s voice, other considerations are absent. Mills’ approach to textual analysis provides a useful framework for considering a broader range of perspectives and contextualizing editorial and linguistic choices within the cultural norms and ideologies of the source text.

Klein, Laura. What Gets Counted Counts. Data Feminism. MIT Press, 2020.

Mills, Sara. Post-feminist Text Analysis. Language and Literature, 1998, p. 241.

Taivalkoski-Shilov, Kristiina. Ethical issues regarding machine(-assisted) translation of literary texts. Perspectives 27.5 (2019): 689-703. DOI: 10.1080/0907676X.2018.1520907.