Technochauvinism, or the idea that technology is always the superior means of attaining an end, is a flawed ideology that has a disturbing amount of overlap with traditional male chauvinism. A common opinion among male chauvinists is that women are inferior to men due to some sort of emotional fragility that prevents them from being as logical as men. With technochauvinism, it is thought that the computer should reign supreme due to its ability to reduce any issue to supposedly-objective, unbiased numbers and mathematics. Technochauvinism, by ignoring or otherwise cutting out human components of analysis and problem solving, can’t help but ignore or cut out concepts such as culture, race, and gender.

Digital technology is created by human beings: human beings with biases and emotions. A computer error is largely the direct result of human error. This paper aims to not only show how male chauvinism can dangerously factor into technochauvinism, but also show that technochauvinism “on its own” impedes feminism in ways not unlike traditional male chauvinism. Ultimately, I wish to present an argument that in order for feminist digital text analysis to be performed, it must be approached in a manner that avoids technochauvinist bias or in a manner where one is aware of technochauvinist bias: to allow a feminist analysis to be affected by technochauvinist bias is undesirable in the same manner as allowing a feminist analysis to be affected by male chauvinist bias.

This week, I examined Fleshing Out Models of Gender in English-Language Novels (1850 – 2000) by Jonathan Y. Chen. This piece interestingly examines a semi-literal sort of “fleshing-out” – that is, Cheng discusses how anatomy factors into characterization and the presentation and representation of characters, and the relationship between this and gender. Cheng also examines how models of gender have changed over the 150 years proceeding 1850 and addresses notions of heteronormativity and binary gender.

In his introduction, Cheng writes that “it is difficult to overstate the significance of the body in scholarly accounts of gender” but that “[anatomical] details have long played a larger role in the representation of women” (Cheng 2-3). He also comments that “[anatomical] characteristics have increasingly been deployed along gendered lines until only very recently” but that “men and women are increasingly embodied using different words,” to the point of descriptions of actions such as “clasping one’s hand” are indicative of gender (Cheng 3). In brief, Cheng asserts that as time goes on, anatomical descriptions become more common in writing.

In his second section, titled Methods and Limitations, Cheng uses a modified version of BookNLP, a natural language processing pipeline, to perform what is largely supervised text analysis of the works in his corpus (clearly stated to be over 13000 English language novels on page 7). He posts images of several samples of his process, including an array depicting locations of references to a character in text (in terms of page number), the pronouns or proper nouns that those references consist of, and the way those references are tied back to the character in question. Cheng describes “[adding] onto [characters’ models] by extracting additional words that physically describe each character” and “[gathering] the verbs and adjectives modifying their bodily features” (Cheng 4).

Essentially, Cheng attempts to associate verbs and adjectives with parts of the body, and then associate all of those and their combinations with a gender. Cheng gives the example of the phrase his hands compared to the phrase his hands grasped: in this case, the verb “grasp” would be associated with the noun “hands” and both words, as well as the combination of the two words, would be associated with the male gender. Of course however, his results use the entire corpus. If he were to encounter three instances of “her hands grasped” later on, it would shift the words towards femininity for the purposes of the analysis.

In terms of limitations, Cheng mentions that “labelled as either feminine, masculine, or unknown” isn’t sufficient to “capture the complexity of gender identity” (Cheng 6). However, he takes into account that his corpus includes works from a time of much more rigid gender norms, thus almost using this limitation to his advantage. Additionally, he admits that pronouns such as “I,” especially in a vacuum, are not sufficient to present a clear gender identity. As a result, he is unable to count such characters’ physical features in a matter applicable to his study.

In the section entitled Embodying Fictional Men and Women, 1850 – 2000, Cheng displays graphs of his results, depicting a trend he describes as “body language [becoming] a growing aspect of all characters as we get closer to the twenty-first century” – on one table of results, the slope (“shown to provide sense of rate of increase”) of the data covering the percentage of physical description of men over time is over double that of the percentage of physical description of women over time (Cheng 9). Later, he posts two graphs comparing the use of body language by gender of characters, but segregated by the gender of the writer of the pieces. Interestingly, both of these graphs show the same general trend as the first graph. However, Cheng comments that “the correlation has dropped a fair amount for female characters written by women” – he keeps in mind the possibility of this being because of a reduce sample size, but comments that there could be “a less poignant relationship between historical progression and the amount of physical description attributed to women”(Cheng 14-15).

Cheng’s last sections before his conclusion are Gendering the Body and Transformations of the Gendered Body. The former presents a very interesting graph showing the accuracy to which text analysis models can predict gender from physical description of texts from given years. While showcasing a lack of accuracy from the model may seem counterproductive without context, Cheng does so to make an intriguing point – at some points in history, specifically towards either end of the 1850-2000 range, gender was harder to predict from physical description. This is supported by the graph showing the least accuracy to the extremes of the X-axis, and the most accuracy towards the middle-right, around 1955.

In his conclusion, Cheng admits that he “[doesn’t] want to make it seem like [he has] provided a complete solution,” but rather that he “merely sketched out one way of analyzing the varied relationship between character and gender” that could very well be considered incomplete (Cheng 29). This may relate back to something he says in a prior section: that some of the analysis “provides a lot of avenues for future research” (Cheng 24).

All in all, I really liked Cheng’s piece. The subject matter of the piece is something I’d like to continue to study and look into more; perhaps what interested me most was how going forward, the models Cheng used had more trouble predicting gender. I’m also curious about how one could theoretically conduct a similar study, but one that accommodates for first-person pieces and character. Certainly, Fleshing Out Models of Gender in English-Language Novels (1850 – 2000) will be something I think back to if I ever have to do research on a related topic.

Each of this week’s readings had something stick out to me above the rest of content in the text. The idea of technochauvinism, at least to me, is very intriguing, and was thus something I had in my head throughout my exploration of the texts. Admittedly, I read beyond the assigned pages in Chun’s piece, but if nothing else, this broadened my understanding of quite a few topics, technochauvinism included.

First, on the very first page of Wendy Chun’s Pattern Discrimination, she asks, quite simply, “what is recognition?” She then presents a thought-provoking comparison: the difficulty of a police officer hailing a single person on the street, compared to the difficulty of a police officer hailing a number of people equal to the number of bits traveling the internet per second (supposedly at the time of writing, 414 trillion bits) (Chun 1). In short, this demonstrates the necessity of pattern recognition, but it also sets a subtle precedent for what’s to come. Indeed, it was this comparison that led me to read beyond the assigned pages, as I saw it when I was first scrolling through the .pdf of the text. While it may simply be a product of the times, the usage of a police officer in the comparison is disturbingly fitting with the subjects presented later in the text, specifically in the realm of the “discrimination” mentioned in the title.

While Chun doesn’t directly discuss technochauvinism, much of what she writes does concern it, especially when examined with the previous comparison in mind. Essentially, a police officer is a human being, and thus is subject to emotion and human error. However, she regularly makes points about the problems with pattern recognition – for instance, about hermeneutics. Additionally, human beings are prone to bias, but Chun explicitly states that “objective analytics, devoid of any interpretation and thus of any bias, does not exist,” or to elaborate, even if a computer were able to make “superior,” unbiased analyses, upon being interpreted by a human being, they would immediately become biased (Chun 35). Even if the computer itself is unbiased, it does not matter, as the humans using the computer are biased.

On the subject of technochauvinism, Meredith Broussard’s Artificial Unintelligence: How Computers Misunderstand the World defines the term as the idea that technology is “always the solution” (Broussard 8). Broussard describes technochauvinism as a “flawed assumption,” a “red flag” (Broussard 7) and as something “often accompanied by fellow-traveler beliefs such as Ayn Randian meritocracy,” specifically citing the related, flawed idea that computers being more “‘objective'” due to their ability to reduce information down to relatively basic math (Broussard 8). Broussard’s discussions of perceived objectivity of computers have a significant amount of clear overlap with Chun’s, to be sure. Similar to how Broussard tells about how her friend’s assumptions about technological superiority stuck with her, the very term “technochauvinism” has stuck with me from the first time I heard it in class some time back.

Broussard refutes the idea that computers are better due to some form of “objectivity” through showing by way of example that computers constructed by humans capable of error (that is, all humans) are subject to the errors and biases of said humans. She writes that a “problem is usually in the machine somewhere” as a result of “poorly designed or tested code” – in other words, that the problem with a computer is the fault of the human that designed it (Broussard 8). One can say that a computer is powerfully “objective” as much as one wants, but even if this is the case, the “objective” data a computer produces is rendered moot when viewed by a human being with subjective bias or the capacity for error (again, all humans) and even then, this is assuming that this “objective” data is not influenced by computer errors (in fact, human in origin).

I want to bring to mind the idea of the platonic ideal, briefly, as I’ve noticed an interesting connection to it. Through the eyes of a technochauvinist, through a technochauvinist lens, a computer is for most intents and purposes something more objective, and thus powerful, than any human: something a human cannot replicate the function of perfectly, similar to how a human cannot perfectly reproduce a platonic ideal. If one claims a computer is a source of perfectly objective information, when that information is interpreted or reproduced by a human, it stops being perfectly objective, just as how humans attempting to replicating a platonic ideal create content further from it. This has made me wonder about the connections between computers, objectivity, technochauvinism, derivative content (parody, pastche, etc.), and rhizomatics as a whole.