As I noted in Case Study #2, Google Analytics (GA) appears most often in scholarship as a black-boxed application that reports (presumed accurate) visitor frequency and browsing behavior on websites. Websites are said by be “successful” in terms of reported visitor traffic to the site, number of pages viewed while on the site, length of browsing session, and additional metrics and dimensions. Few questions are asked of the application itself; its results are considered authoritative and accurate.
For this literature review, I sought scholarship that challenges the assumption of accuracy or convenience of GA data, either in term of collecting, configuring, processing, or reporting data. I also shifted my focus from searching in social sciences and humanities databases to searching in computer sciences-related databases. The results were mixed. On one hand, I found more scholarship that questioned Google Analytics and web/digital analytics in general; on the other hand, I found the scholarship less thorough than humanities or social sciences research.
Dhiman and Quach (2012) report briefly on the rationale and results of a workshop at CASCON ‘12 (Center for Advanced Studies on Collaborative Research) introducing Google’s Go and Dart, two applications under development (at the time) to enable “better analytics” and “better applications” (p. 253). The challenge Dhiman and Quach identify related to GA is that “in a world where there is an emergence of extensive use of analytics, data and fact-based decision making, spontaneous sorting of data becomes imperative…. [A]nalytics are crucial for knowledge discovery, business growth and technological improvements” (p. 253). Google Go is described as a “language that allows programmers to exploit concurrency in program by providing simple yet powerful features” that “make it an excellent language deploying application on concurrent systems” (p. 253). GA is one of many applications engaged in providing digital performance data; Go appears to provide programmers a language that enables concurrently-operating applications the ability to communicate with one another and to report on multiple application data at the same time. GA and other data-generating tools are implicitly critiqued for reporting data in a delayed and proprietary form that requires a mediating application to collate and report data spontaneously.
Fomitchev (2010) is far more direct in his GA critique. In a two-page poster presented at the 9th International Conference on World Wide Web, Fomitchev identifies specific inaccuracies in GA’s collecting of recurring website traffic using cookies. Specifically, Fomitchev finds that “Google Analytics ‘absolute unique visitor’ measure is shown to produce a similar 6x overestimation” of unique visitors (p. 1093). Based in comparative studies that collect recurrent visitor data via multiple methods, Fomitchev elaborates that “Google’s ‘absolute unique visitors’ are not at all unique: the inflation depends on the visitation frequency and grows linearly with time” (p. 1094, emphasis original). Given the potential, even likely, inflation of unique visitor numbers in GA reporting, Fomitchev concludes that the “discrepancy between unique cookies and unique visitors eases doubts in the accuracy of published unique visitor stats used to solicit advertising money” (p. 1094). While the critique of GA collecting methods is explicit, the implicit critique of using GA unique visitor reports to solicit funds for advertising seems more damning. GA as a free service must be monetized in Google ledgers, and advertising is where Google excels. If its reported data are inaccurate, its ethical foundation on accurate reporting (accuracy that is taken for granted, as shown in most studies) becomes suspect.
Back to the OoS
When I re-proposed Google Analytics as my object of study, I narrowed my discussion of GA to its data model and its activities. Both Dhiman and Quach (2012) and Fomitchev (2010) offer meaningful connections between GA and my theoretical lens, Castells’ (2010) social network theory. Dhiman and Quach reiterate the validity of Castells’ “space of flows” and “timeless time” in their needs assessment for a programming language that demonstrates “lightweight concurrency” in its ability to create sets of “lightweight communicating processes” between various programs running and reporting simultaneously (p. 254). Fomitchev (2010) corroborates Castells’ construction of “real virtuality” in which the local and the global function interchangeably and simultaneously, recognizing that GA, a global analytics application, is “fooled by periodic [local] cookie clearing and the multitude of [local] Internet access locations/devices…” (p. 1094).
Defining Google Analytics via Social Network Theory
Castells (2010) considers technology to be society (p. 5). While this seems extreme — I’d be more willing to accept technology as an aspect of society — the result is that GA can be considered social. As an information technology, GA creates active connections between websites (data collection), Google data centers (data configuring and processing) including aggregated tables (processing), and GA administrator accounts (configuring and reporting). These active connections collect, mediate (configure and process), and report on the three aspects of the GA data model consisting of users, sessions, and interactions. These connections represent social actions. So Castells (2010) might define GA as a global informational network (p. 77) that collects data from and reports data to local nodes (websites). Google servers where data are configured and processed might be consider mega-nodes (xxxviii) that, through the iterative process of increasing user visits and interaction by improving website design and content based on GA reported results, impose global logic on the local (xxxix).
Nodes in Google Analytics
Individual websites, GA account administrators, and website visitors are local nodes in the global informational network. Google data center servers are mega-nodes in the network. Google employees who program GA and maintain Google servers and centers are localized nodes in the global network. Google’s data centers are located in a variety of locations that include North America, South America, Europe, and Asia. Several are found in Castells’ (2010) “milieux of innovation” (p. 419) including Taiwan, Singapore, and Chile. Others are found in what appear to be unlikely global spaces, including Council Bluffs, Iowa, and Mayes County, Oklahoma. These locations reiterate Castells’ insistence that local and global are not mutually exclusive polar opposites; rather, the new industrial system is neither global or local, but a new way of constructing local and global dynamics (p. 423). Websites, administrators, visitors, servers, and employees are simultaneously localized nodes (even the the mega-nodes are situated in space and time) in the global informational network.
Agency among Google Analytics Nodes
GA account administrators and website visitors have the greatest level of agency in the network, while Google employees exert limited agency within the confines of their labor relationships and conditions. Account administrators would likely be considered among Castells’ (2010) “managerial elites” (p. 445), while Google employees who maintain and program the servers might be part of Castells’ disposable labor force (p. 295). Account administrators have the authority to configure GA data, including the ability to filter out results, narrow data collection according to metrics and dimensions, and even integrate external digital metrics in GA. This authority is not, of course, the authority of Google’s corporate structure and hierarchy, but within the boundaries of GA data model and activities, account administrators exude authority. Website visitors may choose to visit, or not visit, any given website, once or more than once (meaning a single session or multiple sessions). This agency includes the power to intentionally separate themselves from the network, meaning that, for users, they only enter into the network as a node when they visit the tracked website. Interestingly, only the GA administrator has authority to eliminate users from the network; account configurations may filter out visitors along several dimensions.
Nodal Situation and Relation
Nodes are locally situated. While simultaneously part of the global informational economy, all of the nodes in the GA network are situated in a space and time. This simultaneous here/there compression of space and time is the origin of Castells’ (2010) “space of flows” (p. 408) and “timeless time” (p. 460). Websites are simultaneously hosted on physical servers around the world and locally viewed on specific platforms and media. Users are simultaneously accessing global data in territorial space on hardware. GA administrators are situated while configuring accounts and loading reports from the cloud. Google data centers are situated in specific locations, but they collect and process global data from local spaces and times. Google employees are culturally and territorially situated in the global Google labor pool.
Data rarely travels along parallel paths in the GA data model or GA activities. Website visit data are collected in the data model — user, session, and interaction data — and sent to Google data centers for processing and configuration. Other than writing unique user identification data onto cookies on users’ browsers or apps, little data travels from GA to users. Website content is indirectly affected by GA reports configured and read by GA administrators, but within the GA activity network, websites are unaffected by GA activity on the data model. Beyond the boundaries of the OoS, of course, Google serves plenty of data, in the form of ads, back to users. But that’s now beyond the scope of this study.
Movement in the Network
Data moves in GA. More specifically, data in the GA data model moves in GA. Data are initiated by users visiting tracked websites. Specific frameworks must be in place for connections to occur and data in the data model to be collected. Namely, websites must contain GA tracking code, embedded in the website code through the agency of the GA administrator. The embedded GA tracking code enables, and the web browser and hardware afford (Norman, n.d.), the user to initiate a tracking pixel (gif) and generate data to be collected in the GA data model. Once collected, the data are configured (by the account administrator and by the GA algorithms), processed (in a largely opaque manner) and collated in aggregated data tables, and reported in visual and tabular representations. In Castells’ (2010) terms, data represent flow in the GA network (p. 442). That data is both spatial and temporal (it comes from and is attached to a specific territory and represents a specific, chronological activity), but it is also entirely global and digital.
Content in the Network
Data are collected and packaged — literally, in a gif image pixel — in parameters relating to user, session, and interaction. The GA tracking code encodes data and sends it to Google data centers where the data are decoded, configured based on administrator preferences, processed and repackaged in aggregated data tables, and made available to the account administrators. The reporting function remediates the data in visual and tabular formats for ease of reading and use. While the data reported are considered authoritative and authentic, the actual processing function remains largely proprietary, with only end results available to extrapolate what processing actually occurs. This black boxed processing function seems unlikely to represent Latour’s (2005) intermediary; as Fomitchev (2010) claims, there are probably processing functions that result in highly mediated, possibly even inaccurate, results. Castells (2010) would likely measure GA performance based on “its connectedness, that is, its structural ability to facilitate noise-free communication between its components” (p. 187). I hope we will see increased academic scrutiny focused on this perceived intermediary function in GA, even as we scholars rely on its results.
Birth and Death of a Network
Castells (2010) indicates that global informational networks emerge within milieux of innovation. These main centers of innovation are generally the largest metropolitan areas of the industrial age (p. 66), able to “generate synergy on the basis of knowledge and information, directly related to industrial production and commercial applications” (p. 67), and combine the efforts of the state and entrepreneurs (p. 69). Nodes on the network get ignored (and therefore cease to be part of the network) when they are perceived, by either the network or by its managerial elites, to have little value to the network itself (p. 134). The GA network grows as more nodes are added, either as users or as web pages with tracking code. GA administrators have agency to kill network nodes by removing tracking code from pages, or by directing IT managers to remove poorly performing web pages. Users have agency to quit visiting a website, thereby removing its value to the person. While many other actions by agents outside the GA network may affect the growth or dissolution of the network, they are outside the boundaries of the GA activity and data model.
Boundaries of Discussion
Two sets of boundaries apply. First, the boundaries I set in re-proposing my object of study, namely limiting the application of theory to GA’s activity and data model. By narrowing my object of study, I believe I’ve given myself the ability to tackle each aspect of the theory’s application to GA more specifically and directly. The result is greater clarity in describing GA function and in applying particular aspects of theory to the object.
Second, Castells sets some boundaries to the application. While Castells addresses the local, he tends to discuss localization in terms of groups rather than individuals. In this way, Castells more closely resembles ecological theories that apply to organism categories rather than to individual organisms. He regularly refers to groups of people and nodes: the managerial elites (rather than individual leaders), the technological revolution (rather than revolutionary technology pioneers), and the global and local economy (rather than the economic wellbeing of the individual small business owner). The result is that I can’t really address the individual user as a single agent in GA. Then again, this is hardly a hardship, in that GA aggregates data and anonymizes identities. GA, too, resembles an ecological theory rather than a rhetorical theory; it focuses on profiles of territorially localized users rather than individual users in a specific city. As a result, Castells and GA match rather nicely in defining the boundaries of the discussion. In fact, I’d argue that GA (and Google more broadly) represent precisely the network society Castells defined in his text. It’s interesting that he didn’t predict or recognize the rise of Google as I would have expected him to do in his 2010 preface. And Castells’ (2010) discussion of communication media clearly did not predict the popularity or ubiquity of Google’s YouTube on the network as a differentiated medium whose content is driven by user tastes and users-as-producers (p. 399).
Castells claims that his three-volume series did not try, and is not trying, to predict future evolution of the network. He also claims to avoid ethical judgments on the managerial elites’ treatment of those lacking connectivity in the global network. I found neither claim satisfactory. As GA “black boxes” processes that need to be problematized, so Castells “black boxes” prediction and judgment as processes without taking personal responsibility. In this way, too, Castells and GA are good matches.
Castells, M. (2010). The rise of the network society [2nd edition with a new preface]. Chichester, UK: Wiley-Blackwell.
Dhiman, K., & Quach, B. (2012). Google’s Go and Dart: Parallelism and structured web development for better analytics and applications. In Proceedings of the 2012 Conference of the Center for Advanced Studies on Collaborative Research, (pp. 253-254). Riverton, NJ: IBM Corporation.
Fomitchev, M. I. (2010, April 26). How Google Analytics and conventional cookie tracking techniques overestimate unique visitors [Poster]. In Proceedings of the 19th International Conference on World Wide Web, (pp. 1093-1094). New York, NY: Association for Computing Machinery.
Google Data Centers. (N.d.). Data center locations. Retrieved from http://www.google.com/about/datacenters/inside/locations/index.html
Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford, UK: Oxford University Press. Clarendon Lectures in Management Studies
Norman, D. (n.d.). Affordances and design. Don Norman Designs. Retrieved from http://www.jnd.org/dn.mss/affordances_and_desi.html
Literature Review: Google Analytics, My Beloved OoS
In general, researchers appear to use Google Analytics™ (GA) web analytics service as a tool for measuring web visits and, to an extent, visitor behavior. In discursive terms, GA collects and visualizes an archive of traces of user interactions with web pages. The discursive activity of visiting (and, presumably, reading) a web page is seldom referenced in research that uses GA for measurement; instead, the archival trace of the discursive activity gets captured, archived, and visualized.
Most research uses an enthymeme that reads something like this: GA data can help developers improve websites. For example, Kirk et al. (2012), in an article seeking to monitor user engagement in an Internet-delivered genetics education resource developed for nurses, report that GA “informs approaches to enhancing visibility of the website; provides an indicator of engagement with genetics-genomics both nationally and globally; [and] informs future expansion of the site as a global resource for health professional education” (p. 559). Similarly, Mc Guckin & Crowley (2012), in an article evaluating the impact of an online cyber-bullying training resource, the CyberTraining Project, report that GA data have “allowed for the project team to further understand how best to optimize the product (i.e., the Website and the eBook) for ease of access and navigation by unique and referred users” (p. 629). Focusing more specifically on GA reporting over time, Plaza (2009) notes that “GA tells the web owner how visitors found the site and how they interact with it. Users will be able to compare the behaviour of visitors who were referred from search engines and emails, from referring sites and direct visits, and thus gain insight into how to improve the site’s content and design” (p. 475). Missing from the enthymeme are assumptions that connect GA to improved websites, assumptions that can be phrased in questions about the relationship between GA, website visitors, and website developers: What data are provided by GA that can directly relate to specific improvements in website design? What user behaviors can and should be examined via GA to evaluate the success of the website? What benchmarks should developers set to measure success or failure? While these questions are not ignored in research that uses GA reporting, they are not directly or specifically addressed. As a result, readers miss out on key assumptions that researchers make about specific ways the data provided by GA reports can and will be used to make concrete changes to website design and structure.
Bruno Latour’s (2005) introduction to actor-network-theory (ANT) identifies transporters of meaning among connections as “mediators” or “intermediaries.” An intermediary “transports meaning or force without transformation” while mediators “transform, translate, distort, and modify the meaning or the elements they are supposed to carry” (p. 39). When researchers present GA as a means of measuring user interaction with websites, they generally describe GA as an intermediary. By describing GA as an intermediary, researchers ignore, potentially to their peril, the mediating potential of GA reports. For example, Dahmen & Sarraf (2009), reporting visitor analytics of an online art museum exhibition, claim that “through the use of Google Analytics, this research seeks to understand how the public used the Web representation of the special exhibition” (p. 2). Their report represents GA data as authoritative and unmediated; the GA interface that visualizes and reports visit data is accepted as accurate, without comment. Mc Guckin & Crowley (2012) take a step toward recognizing the potential mediating effects of GA reports by claiming to “ascertain the efficacy of GA as an effective resource for measuring the impact of the CyberTraining project” (p. 628), but they conclude, “Such information [provided by GA] proves valuable in the iterative development and dissemination of the project and has, directly, informed the planning of the new CT4P project” (p. 629). GA is considered a blackboxed intermediary for reporting web visits. In other words, current research offers little theoretical perspective on the potential mediating effects GA may have on the data it reports and visualizes. This blog post seeks to remedy that omission by applying both ANT and cultural-historical activity theory (CHAT) to Google Analytics and the data it provides on visitor interactions with the website of the University of Richmond School of Professional and Continuing Studies (SPCS).
An OoS on the LOoSe
One of the most interesting aspects of using GA as my object of study (OoS) is that it remains a product continually in production. Although Google does not address it explicitly, it’s become clear that Google is working to make GA a digital analytics platform that expands well beyond the measurement of interactions on websites. I’m working toward a certificate of completion for Google Analytics Platform Principles (2014) as a followup to a certificate of completion I received for Digital Analytics Fundamentals (2013), and both of these online learning modules address Google Analytics as a broad-based digital analytics platform that handles data from a wide array of sources, even non-Internet-connected applications and appliances. The result, as I’ve experienced it, is that the Google Analytics Platform (yes, that’s the proper noun) is expanding its reach and scope on a weekly, perhaps even daily, basis.
This makes applying activity theories like the cultural-historical remapping of rhetorical theory (CHAT) and actor-network-theory (ANT) quite comfortable. GA as OoS is itself in active flux, continually redefining (perhaps more accurately expanding) itself for a fast-changing connected world.
ChOoSing a Definition
CHAT might describe GA as a representation of practices within a laminated chronotope. As a tool that measures interactions between visitors and web pages, GA collects the results of “mediated activity:… action and cognition [that] are distributed over time and space among people, artifacts, and environments and thus also laminated, as multiple frames or fields co-exist in any situated act” (Prior et al., 2007, emphasis original). The action that gets represented as a visit in GA is loading a specific web page. Cognition gets represented in the action of following a link on a specific page to load a new page or resource. This activity is collected over time in a session, defined in GA as the time within 30 minutes a single visitor, identified by an anonymous, unique identifier and saved in a first-party cookie (“Platform Principles,” 2014) remains engaged within a surveilled website before leaving that domain or expiring the session time. GA represents all of the activity within that session in an aggregated visualization. Session data are collected over time and are the result of laminated activity among people, artifacts (like web pages) and environments (like browsers, computers, mobile devices and the like).
ANT might describe GA as traces of connections among networked actants. Actants captured in a web session might include the visitor, the technological interface (computer/mouse/monitor or mobile device), the web page content and links, the writer of the web content, the host server, the network gateways and cables, and many more too numerous to detail. ANT would likely chafe under the need to define the collecting mechanism itself, however, and suggest that GA might be an artificial data assemblage that needs to be reassembled. Specifically, since GA is a data framework that collects only preselected data points (“Tracking Code Overview,” 2012), GA might be accused of “filtering out” and “disciplining” the data collection: “Recording not filtering out, describing not disciplining, these are the Laws and the Prophets” (Latour 2005, p. 55, emphasis original). More useful might be the preprocessed data collected by Google Analytics servers; processing organizes the web session into a predefined framework, precisely the activity ANT seeks to avoid in its practice.
LOoSe the Nodes
ANT defines nodes as actors, and there are myriad actors (more precisely, actor-networks) at work in GA. From the programmed codes written and interpreted to the software and hardware mediating and displaying web pages to the visitors and writers and programmers to the network providers and databases—ANT accepts any and all of these actants as nodes with the potential of agency. Latour (2005) refers to these objects as “the non-social means mobilized to expand them [the basic social skills] a bit longer” (p. 67) and confesses that ANT will “accept as full-blown actors entities that were explicitly excluded from collective existence by more than one hundred years of social explanation” (p. 69). The implication is that all the technological hardware and software — the GA code, the wired and wireless networks (cables, routers, and servers), and the Google Analytics processes server — work together to enable the web visitor to interact with this creation of the web writer, developer, coder, and marketer. This collective is incorporated at the moment of loading a web page, and its momentary connectivity is both enabled and expanded by agency of the object actors.
Where ROoSt the Nodes?
CHAT locates nodes in hierarchical relationships with one another in the network. Prior et al. (2007) conceive of literate activity producing socialized interaction within the functional system as part of the laminated chronotope of activity in space and time (Take 2: A Cultural-Historical Remapping of Theoretical Activity). In this hierarchy, web visitors are outside the system except during literate activity, defined as interacting with the multimodal text(s) within the site. Web writers, developers, and marketers are members of the functional system where literate activity (defined as creating and instantiating the multimodal text) occurs. The website itself is the functional system; the School gives the system chronological and spatial existence while the University gives the system technological existence. GA collects traces of literate activity among nodes within the functional system of the website, visualized and reported as interactions in space (between pages) and time (within sessions).
ANT flattens the network entirely. Latour’s (2005) conception of ANT works to keep the social flat (pp. 165-172), connecting all of the actor-networks (nodes) within the activity network in a single, non-hierarchical surface. Within GA, this flatness is largely retained within the report. All actor-networks have mediated, translated experiences of web content — there are no intermediary experiences, whether visitor or writer, software or hardware. GA reports a visualization of mediated network activity in a flattened data table. The flattened data table in GA treats the visitor’s web browser or operating system as equally significant to the actor-network represented by the visitor or web writer. Relationships between actors are largely un-disciplined; they are simply reported, regardless of the inherent logic (or lack thereof) in the relationship uncovered.
CHAT stresses an ecological relationship among nodes, limiting that ecology to the natural and material world (Prior et al., 2007). Visitors enter into the functional system of the website and navigate through it. Web writers, developers, and marketers engender the navigation links through the system, giving visitors pathways for narrative production. The website functions as the system, enabling web visits in time and space. The School provides content for the system, while the University provides the localized instantiation of the content in the website. GA records the traces of interactions within the functional system, visualizing them in laminated chronotopes in time and space. GA does not clearly identify the human actors in the network, preferring to aggregate identities. However, GA enables web writers, developers, and marketers to examine the traces of aggregated literate activity by visitors and revise website content and structure accordingly. This provides the opportunity for dialogue among human actors.
ANT stresses incoming connections among interconnected nodes. Latour (2005) frames this according to what it means to be a “whole”: “to be a realistic whole is not an undisputed starting point but the provisional achievement of a composite assemblage” (p. 208). Nodes that have more incoming connections than others are considered more settled and blackboxed, meaning they shift from being merely actors to becoming conduits for the flow of mediators: “an actor-network is what is made to act by a large star-shaped web of mediators flowing in and out of it” (p. 217). Such a star-shaped web of mediators is immediately visible in GA reports: the page in a website that receives the most visits or page views is the most connected page. This page is generally the website’s home page, and its purpose is not to provide content but to allow mediators to flow through it — to allow visitors to find what they’re seeking and connect to it.
WhOoShing through the Network
In GA, visit data — encrypted bits and bytes, assemblages of sequenced zeros and ones — moves from the visitor’s device to the GA server for processing and reporting. The collection process leading up to this movement differs between browsers (mobile and non-mobile) and mobile apps: browsers send data collections with every page load, but mobile apps bundle visit data and send it in timed intervals to protect mobile device battery life. This too simply describes a very complex ecology of network and computer hardware and software that transmits data from web content creators to web visitors to GA servers, but I’m limiting this discussion of movement to data from visitor’s device to GA servers. See the Google Analytics (2014) Academy “Data Collection Overview” video presentation (below) for additional details.
CHAT might describe this movement as distribution in the literate activity of viewing a web page or using a mobile app. Prior et al. (2007) define distribution as “the way particular media, technologies, and social practices disseminate a text and what a particular network signifies” (Mapping Literate Activity). In this case, two distributions occur: the distribution leading to reception (by the web page visitor) and distribution leading to the assemblage of visit data collected for interpretation on GA servers.
ANT might describe this movement as the social. The assemblage of connections from hundreds of thousands of SPCS visitor pageviews flowing into the GA server could be what Latour (2005) calls “the social — at least that part that is calibrated, stabilized, and standardized — [that] is made to circulate inside tiny conduits that can expand only through more instruments, spending, and channels” (p. 241). In this case, the conduits are standardized in the GA’s preselected data points (“Tracking Code Overview,” 2012). When and if GA adds new data points for collection, these tiny conduits would be expanded. This definition also suggests that many other connections remain unsurveyed, Latour’s “plasma.” The assemblage of all connections would be the social fabric of the network.
Meaning Released from the HOoSegow?
CHAT might describe meaning as the result of literate activity in the functional system. Prior et al. (2007) map literate activity as a multidimensional process that can include production, representation, distribution, reception, socialization, activity, and ecology (Mapping Literate Activity). The results of this literate activity are recorded and transmitted from visitor’s devices to GA servers. The meaning of these data points are processed (interpreted) and reported as visualizations. That meaning becomes the basis of analysis; analysis leads to conclusions about visitor behavior, which in turn result in changes to the web content leading to new literate activities.
ANT, on the other hand, ascribes no meaning to the results of CHAT’s literate activity. Latour (2005) remains adamant into the conclusion of Reassembling the Social that the social is dynamic and active, not a substance: “the social is… detected through the surprising movements from one association to the next” (p. 246). As a result, what GA does in processing and visualizing the results of activity in the SPCS website is not about ascribing meaning, but about tracing associations. And because those associations (connections) are mediated by the limited data points collected, the processing done by the GA servers, and the visualizations available, the reassembled social of GA is likely too limited to trace the plasmatic connectivity of the visitor’s web browsing experience.
Networks Emerge, Networks VamOoSe
CHAT and ANT will agree on this: actors initiate, grow, and dissolve networks. Prior et al. (2007) and Latour (2005) build their arguments on the social activities of actors. CHAT engages those actors in literate activity, while ANT engages those actors as connected actor-networks. Only activity on the part of actors can cause the network to emerge. For CHAT, only the activity of web content creators, web developers, database administrators, marketers, and web visitors can generate the first packet of data to flow across the network from visitor device to GA server. For ANT, the list of actors can extend much farther into non-human actants, but the principle remains the same: actors must initiate the network. Actors can grow the network through more visitor sessions — by many measurements, adding visitor sessions and growing session length is my primary professional objective as web manager — and actors can also dissolve the network by removing a web page (authors) or no longer visiting the website (visitors).
GA itself is a fairly limited network. Its boundaries could easily be drawn around the connection between the GA code on the web page or in the mobile app and the GA server. Any other activity that either leads up to the connection or follows the connection — namely writing and viewing a web page or viewing and interpreting GA visualized data — could be seen outside the network. Except that CHAT and ANT seek to problematize such limited perspectives of networks by addressing the activity that enlivens connectivity. So for these two theories, I found myself widening the focus to include the biological (CHAT and ANT) and non-biological (ANT) nodes in the network. This perspective turns into an ecology whose various members are only momentarily connected at the moment of accessing a web page or mobile app. But in that moment, myriad connections reveal actors and build a remarkably complex assemblage of networked components. As a result, I found few limits in CHAT or ANT to addressing GA as my OoS — other than the shortage of meaningful English words that contain the character string “-oos”.
Dahmen, N., & Sarraf, S. (2009). Edward Hopper Goes to the Net: Media Aesthetics and Visitor Analytics of an Online Art Museum Exhibition. Conference Papers — International Communication Association, 1-28.
Digital analytics fundamentals [Online course]. (2013, October). Retrieved from Google Analytics Academy https://analyticsacademy.withgoogle.com/explorer
Google Analytics platform principles [Online course]. (2014, March). Retrieved from Google Analytics Academy https://analyticsacademy.withgoogle.com/explorer
Kirk, M., Morgan, R., Tonkin, E., McDonald, K., & Skirton, H. (2012). An objective approach to evaluating an internet-delivered genetics education resource developed for nurses: Using Google Analytics™ to monitor global visitor engagement. Journal of Research in Nursing, 17(6), 557–579. doi:10.1177/1744987112458669
Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford, UK: Oxford University Press. Clarendon Lectures in Management Studies
Mc Guckin, C., & Crowley, N., (2012). Using Google Analytics to evaluate the impact of the CyberTraining Project. CyberPsychology, Behavior & Social Networking, 15(11), 625-629. doi:10.1089/cyber.2011.0460
Platform principles: Website data collection [Video transcript]. (2014, March). Google Analytics Platform Principles. Retrieved from Google Analytics Academy https://analyticsacademy.withgoogle.com/course02/assets/html/GoogleAnalyticsAcademy-PlatformPrinciples-Lesson2.2-TextLesson.html
Plaza, B. (2009). Monitoring web traffic source effectiveness with Google Analytics: An experiment with time series. Aslib Proceedings, 61(5), 474-482. doi:http://dx.doi.org/10.1108/00012530910989625
Prior, P., Solberg, J., Berry, P., Bellwoar, H., Chewning, B., Lunsford, K. J., Rohan, L., Roozen, K., Sheridan-Rabideau, M. P., Shipka, J., Van Ittersum, D., & Walker, J. R. (2007). Re-situating and re-mediating the Canons: A cultural-historical remapping of rhetorical activity [Multimodal composition]. Kairos, 11(3). Retrieved from http://kairos.technorhetoric.net/11.3/binder.html?topoi/prior-et-al/index.html
Google Analytics. (2014, March 11). Google Analytics Platform Principles – Lesson 2.1 Data collection overview [Video file]. Retrieved from http://youtu.be/qQdPXouWeJE
Tracking code overview [Web page]. (2012, October 29). Google Analytics. Retrieved from Google Developers https://developers.google.com/analytics/resources/concepts/gaConceptsTrackingOverview#howAnalyticsGetsData
[Header image: I’m a Google Analytics Geek: Screen capture of the Google Analytics Academy]
This rubric really is a social construct: class members collaborated (with a great deal of momentum generated by Maury’s contributions) on the beginnings of our rubric. While each of us likely added or removed bits of the collaborative work to personalize the rubric, I’m proud to be part of this socially constructed, class-sourced rubric development process.
We recognized two major areas of focus for the rubric:
- Articulation and contextualization of the theory
- Application of theory to specific OoS (explained with clarity)
After reading the hypertext theory readings, I recommended a third area of focus, which I’ve included in my rubric:
- Mapping of theory to local context (praxis)
While we can apply theory to our OoS, I think it’s important to be able to map the theory to localized instantiations of the OoS. If theory can’t be mapped to specific aspects of practice in the field, then it hardly seems useful (in a pragmatic sense) to the field or its scholars and practitioners. Not that every theory needs a Spinuzzi-like operationalized exemplar to be valid — but we need to be able to identify specific ways that teachers in local contexts will be able to apply theoretical constructs and principles to pedagogy, and how scholars will be able to apply theory to specific recommendations for action in the field.
We also discussed how or whether to assign grades or points to each aspect of the rubric. Most of us chose to avoid assigning grades: our goal was to develop a rubric that could be applied to both assessment and praxis, and my sense is that assessment needs to be localized at the assignment level rather than generalized at the development level (see Discussion below for more on this subject). As a result, I did not include point values, nor would I want to do so without first sharing the rubric with the person to whom I applied it.
My (class-sourced) Theory Application Rubric
Articulated and Contextualized (Theoretical Understanding)
- Theorist(s) who developed the theory
- Influential predecessors to the theory to theorist
- Main premise(s) of the theory and key attributes
- Limitations of the theory
- Relationship to other theories in the field and importance to the field
- Existing canonical or well-respected applications of the theory
Applied to Object of Study and Explained (OoS Understanding, Application)
- OoS contextualized and explained
- Theory attributes mapped to OoS attributes
- Portion(s) of the theory used and discarded, and why
- Contribution to understanding or re-seeing the OoS
- Practical benefits of applying the theory
- Limitations (blind spots) of this theory as applied to this OoS
- Additions to the body of knowledge surrounding OoS and/or the discipline
Mapped to Local Context (Praxis)
- Local context(s) to which theory can be mapped
- Specific person(s) responsible for activated mapping
- Social and political boundaries defined by theory
- Aspects of theory mapped to specific lived experience
- Anticipated social action to be achieved by mapping
- Assessment process of localized mapping defined
Applying the Rubric
Maury applied specific aspects of network construction with Foucaultian theory to LARPs. Below are the results of applying my rubric to her case study.
|Theorist(s) who developed the theory||Yes||Foucault|
|Influential predecessors to the theory to theorist||No||The assignment did not call for the need to contextualize the theorist among others.|
|Main premise(s) of the theory and key attributes||Partially||Foucault offers a broad range of theories; those applicable to the OoS were appropriately selected.|
|Limitations of the theory||Partially||The limitations of the theory may have been demonstrated by absence in the case study.|
|Relationship to other theories in the field and importance to the field||No||This was not a required component of the assignment.|
|Existing canonical or well-respected applications of the theory||N/A||The scope of the assignment did not require this level of exploration of the theory.|
|OoS Understanding & Application|
|OoS contextualized and explained||Yes||Thorough explanation of the OoS and its context made it accessible to a complete noob.|
|Theory attributes mapped to OoS attributes||Yes||Of special note were connection to archive, positivity, absence, and monument.|
|Portion(s) of the theory used and discarded, and why||No||It’s difficult to nail down Foucault to a single theoretical stance or even set of stances; as a result, this is an appropriate omission.|
|Contribution to understanding or re-articulating the OoS||Yes||Among the strongest aspects of the case study. Application revealed relational and contingent character of the game’s discourse.|
|Practical benefits of applying the theory||Yes||Among benefits noted are recognizing the change in meaning that occurs as the game is played.|
|Limitations (blind spots) of this theory as applied to this OoS||No||Given the broad range of theoretical position Foucault offers, it’s difficult to identify limitations.|
|Additions to the body of knowledge surrounding OoS and/or the discipline||Yes||The networked description of the OoS via Foucault focuses attention on specific connections within the game, and it broadens an understanding of Foucault’s archive and monument.|
|Local context(s) to which theory can be mapped||Yes||LARP as distinguished from cosplay, historical re-enactment, creative anachronism, and boffer-style LARP.|
|Specific person(s) responsible for activated mapping||Yes||Very detailed; notable are Game Masters along with many other actors on the network.|
|Social and political boundaries defined by theory||Yes||The field of game play is clearly articulated and connected to the field of discourse.|
|Aspects of theory mapped to specific lived experience||Yes||Another strength of the case study, mapping specific lived experiences of LARP to theoretical aspects.|
|Anticipated social action to be achieved by mapping||Yes||Closing statement addresses the specific social action expected: multiplicity of discourse emerging from a single LARP.|
|Assessment process of localized mapping defined||Yes||In the same closing statement, successful mapping with be demonstrated by multiple discourses from a single LARP.|
The rubric we crowd sourced was intended to address broadly the way a theory is constructed in its time-space and context. Since our assignment was to apply a theory that we had all worked on together in class, neither the assignment nor our execution was expected to spend a great deal of time explaining the key components of the theory, its place among theories, or other contexts related to the theory itself. It was assumed that we’d bring to the assignment that understanding without having to articulate it in the blog post.
However, as a hermeneutic, the rubric offers a useful set of tools for assessing and presenting major theoretical aspects to a reader. Of particular importance as we move forward in our case studies will be explaining more of the influential context of the theory — its predecessors, its influences, its turns and negations, its relationship to other theoretical stances. And a conference paper-length application would certainly be expected to use a literature review to place the theoretical stance(s) in appropriate context. As a result, although this case study implicitly precluded most of the contextual background of Foucaultian theory, the rubric itself is likely hermeneutically sound.
OoS Application and Explanation
In the case of Foucault, nailing down a single theoretical stance, or even a set of theoretical positions, is quite difficult. As a result, omitting some of Foucault’s theoretical positions is necessary in anything but a monograph-length study (and even then, I’m not sure). These omissions don’t necessarily mean they don’t apply to the OoS or that there are no mappings between the theory and the OoS. I take these omissions to be practical realities, and would likely consider them so even in a graded assignment (unless major issues were left unaddressed, like statements or discursive formation). That same breadth of theoretical perspective necessitates the OoS itself to define its limits within the frame of theoretical reference. In a more narrowly focused theoretical stance, I’d expect more explicit statements about the OoS boundaries as defined by the theory. In the case of Foucault, I sensed little of LARPs that Foucault would not address. While this was never explicitly stated or even implied in the analysis, the results speak for themselves — there is no shortage of LARP when applying Foucault. As a result, even though the application does not always address every aspect of the rubric, I don’t think the rubric is faulty.
I surprised myself in finding the Praxis section of the rubric the most informative and applicable section of the rubric. I found concrete mappings between theory and localized context. I don’t consider this section to repeat the OoS application and explanation section; to me, the object of study is not necessarily localized. In the case of LARPs, for example, the localized mapping went so far as to specify a single LARP (Three Muskateers), while the OoS itself remained a more general discussion of LARPs. However, even this general discussion worked to localize the LARP by differentiating it from other similar activities. My mapping the OoS in lived experience to theory, both LARP and theoretical understanding benefitted. As a reader with no LARP experience, the localized mapping offered a clear theoretical underpinning to the concept and practice of the LARP while clarifying in concrete examples some of the more difficult concepts of Foucaultian theory. Mapping theory to localized experience offered a win/win experience for me as reader, and I believe that same experience applies to extending knowledge and understanding of both fields.
This rubric, like all others, requires a flexible, local application bound up in real experience. The fact that the assignment did not fulfill all requirements of the rubric makes neither the assignment nor the rubric unsuccessful. The assignment called for different expectations than the rubric (which, of course, reveals in practice the importance of developing rubrics prior to, rather than in response to, assignments), so the rubric could not be fully applied to the assignment. Furthermore, the rubric addressed a broader conception of theory and OoS than the format and length of the assignment could achieve. I believe it’s important to recognize ways the rubric can’t or won’t measure exactly what it needs to measure in each and every instance. Every assignment — and every response to every assignment — is a localization, and each requires a flexible application of the rubric. This does not make the rubric an inefficient or inaccurate measurement tool; on the contrary, it reveals the value and significance of local context in measurement.