José Luis Cano Jr.
I am arguing for a relationship between people and places—the places we populate and the places within which we circulate—in order to suggest that we not only define these spaces but are also, in part, defined by them.
— Adela C. Licona, Zines in Third Space
Given the whiteness found in web-based and digital scholarship, I feel more comfortable articulating my own brown digital space. While Adela C. Licona (2012) argued for the "relationship between people and places" (p. 9), I extend her position to include the relationship between people, places, and technologies. My experiences as an adjunct instructor at the community college prompted this inquiry into people, learning outcomes, language, and land to improve my instructional practice. Therefore, my perception on these matters roots itself in my experiences in this material space. I articulate this digital space—that is, this webtext—as an assemblage of brown digital praxis in its restructuring of technological relationships, its antagonism toward whiteness, its repurposing of state-sanctioned data, and its centering of college students in the Brownsville–Matamoros area.
As an assemblage of brown digital praxis, the coding in this webtext and its digital maps work toward inclusion of languages and dialects beyond WME in comp courses. la paperson (2017) wrote, "The agency of the scyborg is precisely that it is a reorganizer of institutional machinery; it subverts machinery against the master code of its makers; it rewires machinery to its own intentions" (p. 55). This assemblage of brown digital praxis attempts this task. American Community Survey's (ACS) data on language (U.S. Census Bureau, n.d.-a, 2015) and Texas Higher Education Coordinating Board's (THECB, 2021) learning outcomes serve as the institutional machinery, but I reassemble them for my own uses. For instance, the Leaflet.js coding in the maps included in this webtext works to recode language practices in comp courses through its identification and reassemblage of technologies, or as Licona (2012) described, a "potentially transformative recoding" (p. 19). As the reader will later observe, the boundaries in the U.S. maps remain unchanged because altering them risks standing outside the scope of institutional practice and remain in the realm of imaginative possibilities. In essence, these maps challenge current institutional technologies, but they accomplish this task through institutional rhetoric.
I like to think of things and processes as technologies because this approach makes me feel like I can change or adapt them for my own use. For example, Asao B. Inoue (2010) viewed "writing assessment as technology" to extrapolate and intervene in assessments' racist practices and outcomes (p. 135). I build on this technological foundation that works toward combating racist practices, which means that I don't take race-neutral stances on the impact of technologies, regardless of intention. Technical communication scholar Angela M. Haas (2012) specified, "Technologies are not neutral or objective—nor are the ways that we use them" (p. 287). Moreover, in their discussion of raciolinguistic ideologies, sociolinguists Jonathan Rosa and Nelson Flores (2017) redirected "attention from racialized populations' linguistic practices to hegemonically positioned modes of perception through which these practices are apprehended" (p. 630). The authors found it useful to focus on technologies and institutions—although I consider them one in the same—that apprehend linguistic practice so as to identify and interrogate them.
Consequently, I understand technologies as inclusive of processes, policies, institutions, and information. This view of technology fosters ways to comprehend the assemblages of technologies animating comp courses. This approach informs my practice because I can prioritize and strategize on those technologies that seem breakable (short- or medium-term strategies) and those that appear unbreakable (long-term strategies).
The map works as a powerful technology because it influences the way rhet-comp scholars perceive the relationship between language, land, and comp instruction. la paperson (2017) wrote, "Technologies generate patterns of social relations to land" (p. 5). In large part, comp instructors teach WME because comp instruction in post-secondary settings transpires on U.S. land. Discussing this type of relationship, decolonial studies scholar Walter Mignolo (1996) asserted that the "linking of languages and territories to constitute a particular nation was essentially a move by intellectuals and the state striving for certain types of imagined communities" (p. 183). These imagined communities arise out of educational spaces, such as the comp course, but extend well beyond them, which is a reason I connect comp courses with THECB's learning outcomes and ACS's data on language.
In the United States, maps as a technology inscribe ownership of land for a people who then typically practice a language on this claimed land. In examining the rhetoric of place-names, Ralph Cintron (1997) considered maps as "a subjugation of reality" (p. 21). I feel, however, a potential exists to refashion the map and this relationship. So, I construct two maps with Leaflet.js, an open-source mapping technology. The "U.S. Linguistic Plurality" map and the "Scyborg Map on Language Presence in the United States" provide generative ways of analyzing and imagining technologies of land, language, and instruction. Using ACS's data on language at the county level, the "U.S. Linguistic Plurality" map showcases static data when a user interacts with it. The user can view this information in a stable form that cultivates a feeling of certainty.
Contrastingly, using ACS's data on language at the state level, the "Scyborg Map on Language Presence in the United States" displays shifting data on every user interaction because a "math.random" function infuses this technology with its own intention. This scyborg map seemingly destabilizes the data so as to engage the user in thoughtful reflection and action aimed at combating the prevelance of WME. Stated differently, I analyze the data in a way that doesn't reinforce whiteness as much. (For a closer look on map construction, check out the Notes page.2)