Cross-Border Networks in Writing Studies. Derek Mueller
synchronic career paths. The respondent’s current geographic location is positioned as a hub from which all other geolocations emanate as radial edges or links. The model’s logic was derived from R’s plotting schema.
Desiring better maps and a better platform for exploring these data and connections among them, the next iteration of the maps required learning about geoJSON, a coding specification suited to the challenge of plotting points and lines, which can then be easily imported and overlaid on a more dynamic and interactive map projection. GeoJSON turned out to be a more powerful and flexible alternative to R, but its role in the development of the maps was far more than technical. Exploring geoJSON introduced a second, slightly different logic for plotting career activity.
Figure 7. A simple visual model depicting diachronic career paths. The respondent’s from identification is designated beginning point, with subsequent geolocations plotted sequentially, ending with the respondents current location. The model’s logic was derived from the Mapbox plotting schema involving geoJSON.
Compared to the starburst or hub and spoke in the examples using R, the default presentation of geolocative series in the geoJSON template I adapted appeared like an angling line segment, or a daisy chain. The string of coordinates were entered and subsequently output linearly, which introduced as a counterpart model a different perspective on career activity that, although it still reflected cross-border traversals, showed careers to be more path-like and sequential. Though neither pattern is inherent or prefigured in the programming code, the pairing of R and geoJSON signaled two distinct logics for plotting career activity as relates to geographic coordinates: the synchronic, starburst pattern, and a diachronic, pathway pattern. The pairing of these two coding specifications, moreover, presented variations that changed up the cartographic representation of cross-border activity, but that also deepened the maps’ roles in rethinking cross-border influence in the context of mentorship as it has manifested for many of the scholars who responded to the survey.
Figure 8. Two logics for plotting career activity. Side by side, the synchronic and diachronic models set up as a pair of complementary heuristics for inquiring into the dual nature of career activity as simultaneously distributed and emplaced.
Thus, the experimental map making led to new, fortuitous questions for examining disciplinary activity at a distant scale: Which representation is most telling when tracing networks suggestive of transnational interdependencies?
With these two simple models in mind (and sight) for thinking about how careers unfold, temporally and spatially, I scrapped the flat maps rendered by R and developed two new maps from all of the geographic data collected in the survey using geoJSON only. Complete, interactive versions of both maps are available for reference online, and accessing them directly (via a web browser) is preferable because their interactive affordances offer much more to explore than do the two dimensional screen-based or off-print versions. The maps are online at http://lab.earthwidemoth.com/lines.html (permanent: http://bit.ly/2i2IyIdlines) and http://lab.earthwidemoth.com/path.html (permanent: http://bit.ly/2iUiWSjpath).
Figure 9. A screenshot of the interactive map available at http://lab.earthwidemoth.com/lines.html (permanent: http://bit.ly/2i2IyIdlines), which has been overlaid with geolocative survey data following the synchronic model.
Figure 10. A screenshot of the interactive map available at http://lab.earthwidemoth.com/path.html (permanent: http://bit.ly/2iUiWSjpath), which has been overlaid with geolocative survey data following the diachronic model.
At the default scale, both maps (one based on each career model) look like a skein of densely entangled lines, and although they corroborate the cross-hatching of Canadian-American career activity evident in the survey responses, the maps project data too thickly packed to process with visual ease at the default scale. However, there are a few features in the online versions of the maps that may aid users in making sense of each tightly packed viewport. With the plus symbol, it is possible to zoom in and thereby change the map’s scale. Users can mouseover a line and see the name of the scholar the line documents. Also, the layer selector on the left allows the singling out of smaller samples of data differentiated by stage of career. Lines with orange hues show career paths for survey respondents who completed a PhD before 1996. Red lines are for mid-career scholars. And green lines show the paths and footprints in the respective maps for doctoral students and junior faculty. In effect, although the colour-coding doesn’t provide complete relief from the map’s overcrowding, it does introduce a time series quality that allows us to look for distinct patterns for each group.
While these two exploratory maps figure significantly into the distant approaches attempted in this chapter, and while both provide compelling interactive reports on the aggregate place-to-place career activity of our survey respondents, the separation of diachronic representations from synchronic representations is too limiting, especially for investigating how such maps can at different scales and with a narrower selection of geographic locations tell us about other kinds of networks operating here, such as tacit mentorship networks that play a vital role in the discipline’s ability to reproduce itself. Mentorship networks are notoriously difficult to visualize because, even though mentoring relationships are common, their referential basis is extremely irregular and difficult to confirm. In fact, most disciplinary accounts of mentorship circulate as personal narratives and local accounts of key influences, sponsoring and inviting figures who welcomed a newcomer to the field (viz. Andrea Lunsford’s “The Nature of Composition Studies,” 1991). Mentorship oftentimes surfaces through specific geographic locations and the institutions at their coordinates, but these mentorship relays do not come across clearly enough in one map or the other because mentoring faculty operate from a synchronic position, having themselves already accumulated myriad, distributed affinities whereas students tend to operate more from a diachronic position, in the sense that they are stopping through programs before moving to another location. The point here is that a combination of the two visual models along with compound criteria for defining mentoring relationships offers a unique handle on these emplaced disciplinary networks that span across generations and distances.
To explore this more fully, I created one final map.
Clearing away many lines from the comprehensive maps, the final map combines the diachronic and synchronic models to focus on Dale Jacobs, Associate Professor at the University of Windsor, and eight respondents to the survey who he worked with when they were master’s students at Windsor between 2000 and 2013. Jacobs’s career footprint is represented by a pink line, consistent with his stage of career as coded in the previous maps. This map is accessible online at http://lab.earthwidemoth.com/network.html (permanent: http://bit.ly/2i14Oaqnetwork)
Figure 11. Screenshot of the interactive map available at http://lab.earthwidemoth.com/network.html (permanent: http://bit.ly/2i14Oaqnetwork), which has been overlaid with geolocative survey data focused specifically on Dale Jacobs and respondents he has mentored at the University of Windsor.