A Scyborg Composition Course

José Luis Cano Jr.


1. I use the term White Mainstream English (WME) to follow April Baker-Bell's (2020) position. She wrote, "Following Alim & Smitherman (2012), I use the term White Mainstream English in place of standard English to emphasize how white ways of speaking become the invisible—or better, inaudible—norm" (p. 3). (Return to Introduction)

2. I use data on language from the American Community Survey (ACS), Leaflet.js, and QGIS, so I explain each map below.

U.S. Linguistic Plurality Map

This visualization uses the ACS's "Language Spoken at Home" (TableID: 1601: U.S. Census Bureau, n.d.-a) with county-level boundaries, which reflects the five-year span of 2014–2018. The language categories in the table include English-only, Spanish, Indo-European, Asian and Pacific Island, Other, and Languages Other than English. To rename the terms of engagement with this data, I renamed the category "Languages Other than English" to "Linguistic Plurality." In addition, I revised "Indo-European" to "Other Indo-European" since Spanish and English fall under this previous category. Because this data measures six broad categories, its limitation surfaces in its inability to specify languages at county levels.

For this map, I used the open-source GIS software QGIS in conjunction with the JavaScript library Leaflet.js. I imported a CSV file containing formatted and cleaned ACS's data on language into QGIS and joined it with a shapefile with county-level boundaries. The shapefile draws boundaries for counties, so the counties reflect the language data at this point. Then, I exported this joined layer as a geoJson file to manipulate it in Leaflet.js. With the Leaflet.js library, I created a choropleth map displaying "Linguistic Plurality," a name I changed from the original variable called "Languages Other than English" in the ACS's data on language.

In this map, a user can hover over any county and receive information on that specific county's language percentage and categories: Linguistic Plurality, Spanish, Other Indo-European, Asian and Pacific Island, Other languages, and English-only.

Scyborg Map on Language Presence in the United States

To detail more languages, I selected the ACS's "Detailed Languages Spoken at Home and Ability to Speak English for the Population 5 Years and Over: 2009–2013" (U.S. Census Bureau, 2015). In this table, I focused on the variable that depicts languages spoken at home. While this data provides more languages, the limitation emerges in its geographic scope which sits at the state level. A county-level data on detailed languages exists, but this data fails to account for every county in the United States. Therefore, I decided to use state-level geographic boundaries for detailed languages.

For the state-level map, I worked with Leaflet.js. First, I assigned each geographic state a variable containing languages present in its boundaries. For example, if State X contained 148 languages, I wrote a variable "StateXLanguage" that holds those 148 languages. Hence, upon hovering over, State X displays one out of the 148 languages. Even though states have numerous languages present, I decided to display one language at a time. To allow the computer to make the decision on what language to show, I incorporated a "math.random" function, which randomizes the language to display. la paperson (2017) wrote, "It's that elliptical gear that makes the machine work (for freedom sometimes) by helping the machine (of unfreedom) break down" (p. 55). I consider this a scyborg map, in part, because state-sanctioned data works against its colonizing process. A person and a machine rewire data on language in the context of the comp course. I recognize the way this ACS data typically advances WME dominance in the United States, so I strategically randomize—or the computer randomizes—the data so as to generate an entirely different mode of analysis than a typical linguistic map. Furthermore, the map displays a different language at every hover over with the mouse to augment the strategic destabilization of a WME argument based on frequency/density analysis. This method of analysis raises instructional and ethical questions on the presence of languages in the United States. The map makes visible the hundreds of languages in practice in the US. I interrogate traditional forms of aggregate analysis that simply reinforce WME, and instead, I showcase the slipperiness of data of this data while simultaneously using it to advance plurality of languages.

(Return to "Brown Digital Praxis")

3. The Texas Higher Education Coordinating Board (THECB) oversees every public post-secondary institution in Texas. As such, the THECB (2021) releases a guidebook, entitled ACGM Lower Division Academic Course Guide Manual, listing the learning outcomes for undergrad courses at these institutions. If an instructor decides to teach at a comp I course at a community college in Brownsville, Texas, the instructor will use the same learning outcomes as an instructor teaching comp I at Texas A&M University. Private institutions do not report to the THECB, but through the rhetoric of "peer institutions," private institutions do not veer too far from these practices. For instance, the current private institution where I study outlines five learning outcomes for comp courses, which align well with the THECB's five learning outcomes. (Return to "Breaking Learning Outcomes")

4. I chose the American Community Survey (ACS) because scholars have noted its underlying ideologies. For instance, sociolinguist Jennifer Leeman (2004) viewed the U.S. Census and its data as a technology that forms "the relationship between language and national identity . . . solidifying the tripartite ideological clustering of Whiteness, English, and American Identity" (pp. 517–518). Similarly, Charise Pimentel and Deborah Balzhiser (2012) explained how data analysts interpret data to conflate ethnicity and race, when it comes to Hispanics, in efforts to depict a larger white population. In other words, the ACS serves white supremacy purposes. These ideological presentations of the ACS functions as a public and instructional technology that teaches people to understand WME as the only language to associate with the United States. (Return to "Breaking Learning Outcomes")

5. In their Lower-Division ACGM, the THECB (2021) explained, "The ACGM serves as the academic course inventory for all community, state, and technical colleges in Texas" (p. 5). (Return to "Breaking Learning Outcomes")

6. I used Chart.js to construct the visuals with data on faculty characteristics (TableID: 316.20) from the National Center for Education Statistics and data on educational attainment based on language from the ACS. (Return to "Breaking Learning Outcomes")

7. I seem to conflate spoken proficiency with written proficiency, but that's not the case. I'm drawing a connection between spoken proficiency and the comp course because written literate practices for non-WME do not receive enough attention in the United States, which makes the state of written literacy of non-WME extremely dubious. (Return to "Breaking Learning Outcomes")

8. I make this statement based on the percentages afforded through these technologies. However, I realize that many instructors in these regions subvert these technologies in their instructional practices in efforts to adjust to the rhet-comp needs of students. (Return to "Breaking Learning Outcomes")

9. In their explanation of language use in the United States, the U.S. Census Bureau (n.d.-c) stated, "Most people in the United States speak English and most governmental functions are in English." This rhetorical move places English as the un/official language of practice by sheer number of speakers, so this one statement sets a context for their enterprise by undermining language histories, language practices, and language possibilities. (Return to "A Scyborg Map: Technological Resistance")

10. In "Decolonization Is Not a Metaphor," Eve Tuck and K. Wayne Yang (2012) stated, "When metaphor invades decolonization, it kills the very possibility of decolonization; it recenters whiteness, it resettles theory, it extends innocence to the settler, it entertains settler future" (p. 3). Furthermore, Linda Tuhiwai Smith (2012) asserted, "Maps of the world reinforced our place on the periphery of the world, although we were still considered part of the empire. This included having to learn new names for our own lands" (p. 34). And so, this project is not decolonizing, that is, repatriating land. I believe, however, that this webtext challenges technologies that sustain white supremacy in their inscription of whiteness into land, language, and comp instruction. (Return to "Reassembling Technologies")