José Luis Cano Jr.
In this webtext, I approach the comp course as an assemblage of technologies that inhibits moving beyond White Mainstream English (WME). The assemblage of state-assigned learning outcomes, the data on language from the American Community Survey (ACS), and the comp course reinscribe WME. However, this assemblage of technologies works against itself when reassembled appropriately. Through digital mapping (Leaflet.js), I reassemble these technologies to "break" learning outcomes and imagine more generative relationships between language, land, and comp instruction. In so doing, I advance digital mapping as a technological and analytical approach to remap language relationships in rhet–comp.
The scyborg's medium is assemblage. When we take assemblages seriously as both analytical of power and as the medium for it, then the question becomes, how do you hack assemblages? The scyborg is a sculpture of assemblage—s-he splices one machine to another, de/links apparatuses from/to one another, places machines to work in making new machines, disassembles and reassembles the machine.
— la paperson, A Third University Is Possible
For a moment, I worked as an adjunct instructor of English at a community college in Brownsville, Texas. I enjoyed my time there because of the students. Their upbringings and linguistic arts facilitated inclusion of bilingual assignments and topics related to the Brownsville-Matamoros region. My first semester teaching, one detail caught me off guard. The department required for instructors to submit a document listing each of the five learning outcomes and the percentage of students who met each one. This process started as a way to hold data for accreditation purposes, and this document exposed that my comp course existed in assemblage with technologies seemingly outside my classroom.
In considering bi/multilingual instruction, my issue did not surface from not knowing how to instruct bilingual students at the border or moving beyond White Mainstream English (WME)—I wasn't killing the teaching game or anything, but I was holding my own.1 Rather than an imaginative or instructional block, I encountered a technological block. How do I expand on a comp course that has pre-assigned learning outcomes? And what assemblage of technologies impact my ability to teach comp courses?
Scholarship in rhet–comp expanding on linguistic issues works well at identifying roadblocks in ideology and in-class practice. In fact, the conversation on incorporating languages and dialects beyond WME reaches back a few decades. In "'Students' Right to Their Own Language': A Retrospective," Geneva Smitherman (1995) concluded that this resolution "served its historical time and paved the way for this next evolutionary stage," which she considered the "issue of multiple linguistic voices" (p. 26).
Rhet–comp scholars responded. Bruce Horner and John Trimbur (2002) noticed the components that foster a "sense of inevitability that makes it difficult to imagine writing instruction in any language other than English" (p. 595). Suresh Canagarajah (2013) advanced a paradigm centered on translingual practice, which "holds that it is possible for words to be meshed into another language and still play significant functions for voice, values, and identity" (p. 11). Further, Louis M. Maraj (2020) wrote his monograph Black or Right in his multiple voices. In addition, Sara P. Alvarez (2018) stated that "multilingual writers are now at the center of growing scholarly discussions between the intersections of bi/multilingualism and writing for academic contexts" (p. 342). El Paso–Juárez border scholar Isabel Baca (2019) explained that she teaches "bilingual writing courses," where students can use their home languages (p. 192). Writing on the Rio Grande Valley, Alyssa Cavazos (2019) explored students' perceptions of linguistic matters in comp courses.
These various preoccupations by scholars in rhet–comp signal that the movement beyond WME remains active. They interrogate dominant ideologies, provide alternative modes of conceptualizing language practice, present instructional strategies, and chart new terrains. To continue in the direction of bi/multilingual figurations, rhet–comp must reassemble seemingly external assemblages of technologies imposing whiteness on comp courses.
Reassembling language in rhet–comp builds preferable directions for racialized folks—in my case, brown students at the border. Yet I realize how these assemblages impact other students in related contexts. In specific, amazement and bewilderment strike me when I consider how five learning outcomes systematically incapacitate, at least partially, students and teachers in Texas that fail to practice WME. Consequently, my understanding of the comp courses includes a consideration of technologies and their assemblage since, as an instructor, I don't teach comp courses detached from the external world.
To imagine and practice more generative relationships in comp courses, I prefer to reassemble technologies so as to promote the inclusion of languages beyond WME. I incorporate scholarship sorta outside of rhet–comp. In A Third University Is Possible, la paperson (2017) wrote: "The university is in assemblage. It is imbricated with other assemblages" (p. 62). Many assemblages exist involving the comp course, so I focus on the following assemblage: Texas Higher Education Coordinating Board's (THECB, 2021) comp course learning outcomes and American Community Survey's (ACS) data on language (U.S. Census Bureau, n.d.-a, 2015). The THECB prescribes comp course learning outcomes for public post-secondary institutions in Texas, and the ACS's data on language explicitly and implicitly educates individuals on expectations for language practice in the United States.
I reassemble these components through a mapping technology (Leaflet.js) for two purposes. Using a case study on the U.S.–México border, I juxtapose ACS's data on language and the THECB's comp course learning outcomes to demonstrate current learning outcomes' role in excluding languages and dialects beyond WME. This juxtaposition "breaks" these learning outcomes. Second, a scyborg map remaps the relationship between language, land, and comp instruction. While I provide the ACS's data on language for the map, the code animating the digital map—a "math.random" function—resists a white supremacist approach to language, hence, a scyborg map. Through these moves, I reassemble and (hopefully) subvert institutional machinery that sustains white supremacy.