José Luis Cano Jr.
The scyborg map functions as an assemblage of brown digital praxis in its reassemblage of technologies—Leaflet.js and the American Community Survey's (ACS) data on language (U.S. Census Bureau, n.d.-a, 2015)—that resist whiteness and reinscribe the relationship between language, land, and comp instruction. The scyborg map renders the argument of WME in the United States as unethical and somewhat groundless. It confronts comp instructors with the hundreds of languages exercised in each state, prompting questions rather than providing solutions. Furthermore, the scyborg map serves as an assemblage of brown digital praxis because ACS's data on language and its analysis counteract other analyses that use frequency/density of language presence to reinforce WME.9
In constructing this map, I take advantage of the humanities-based approach to quantitative data. Quantitative data disembodies the person from the collected characteristic. Even the construction of the data collection instrument reduces individuals to specific characteristics. In "Queering and Transing Quantitative Research," rhet–comp scholar G Patterson (2019) shared this apprehension about positivism's approach to "solve problems by relying on disembodied, aggregate data" (p. 54). As a rather brute approach, I simply don't aggregate data on language in the scyborg map because aggregation typically benefits WME and subjugates other languages and dialects. Instead, I display one language at a time, so each language recorded has equal probability of selection. This approach does not recreate the nationalistic one language rhetoric because outcomes constantly morph in the scyborg map.
The scyborg map appears unstable because of these shifting outcomes. Discussing Sylvia Wynter's demonic ground, Carmen Kynard (2012) explained that "Wynter introduces the notion of 'Demonic Grounds' based on theories of math and physics where a system that is in place is called demonic when it does not have an already determined or knowable outcome." In Leaflet.js, I employ a "math.random" function to randomize each outcome at the state level. A finite number of languages inhabit a geographic space, which means unlimited and completely unknowable posibilities do not exist. Yet, a math.random function in the code selects the outcome, hence, unknowable as such to the user.
This unknowability remaps relationships between language, land, and comp instruction. In Demonic Grounds: Black Women and the Cartographies of Struggle, Katherine McKittrick (2006) noted that "existing cartographic rules unjustly organize human hierarchies in place and reify uneven geographies in familiar, seemingly natural ways," but she also noted, "these rules are alterable and there exists a terrain through which different geographic stories can be and are told" (p. 15). This scyborg map accomplishes this particular task of constructing new cartographies of language. For example, in Texas, the outcome may display Tewa language one moment and Comanche upon a second interaction. The languages highlighted depict one possibility out of many. WME receives less visibility. This approach to a digital map challenges assumptions and raises different questions on language practices in comp courses.
The comp course teaches in WME, in part, because of institutions' physical positionings in the United States and the expected language of practice in this land. In the context of a comp course, the scyborg map destabilizes this relationship and practice. Rather than offer solutions, the scyborg map interrogates and expands imaginative, practical, and instructional spheres.
The scyborg map refuses the idea of WME as the default language in comp courses. In its place, the map challenges tacit assumptions of the relationship between language, land, and comp instruction so as to open spaces of imaginative and instructional practice.