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Case Study #4: FrankenTheory

Boundaries in My Analysis of Google Analytics

I am limiting my analysis of Google Analytics as an object of study by focusing on its activities and its data model as reported in terms of dimensions and metrics.

  • Google defines Analytics activity as collection, collation, processing, and reporting.
  • Google describes its data model as consisting of user, session, and interaction.
  • Google collects and reports data in terms of dimensions (“descriptive attribute or characteristic of an object”) and metrics (“Individual elements of a dimension that can be measured as a sum or ratio”) (Google, 2014).

These limits and terms are described in detail in my earlier Re/Proposed Object of Study: Google Analytics blog post.

I chose GA as my object of study because it’s a tool with which I work on a daily basis. I proposed GA as my object of study to my boss, the director of our school’s marketing and communications team, before formally proposing it in class because I wanted approval to use our school’s GA account in my study. I also expected my study to contribute to my understanding and use of GA in web development and management. A deeper understanding of GA as a network has provided both a tool for theoretical exploration and practical application.

Here’s an example of how applied this theoretical study has become. On April 16, with little fanfare, Google announced that it was replacing the term “visit” with the term “session” in its reports. I missed the announcement entirely, so I was surprised while measuring the result of online advertising efforts in our campus newspapers to discover that the “unique visits” metric that I had been using was no longer available; instead, it had been replaced by the “sessions” metric, without the “unique” modifier. I was also surprised to discover that the “unique visits” metric I had been using did not match the “sessions” metric when I re-ran prior reports to test data accuracy reports; “sessions” reported higher numbers than “unique visits” had reported. As we reached the first of May, when I normally complete April reports, I realized the full extent of the terminology change: “unique visits” were no longer being measured. Two plus years of reporting data were potentially compromised as inaccurate, since we report data for month on month and year on year comparisons (e.g. does April 2014 look better than April 2013 in terms of overall unique web visits, and does the calendar year-to-date period of January-April 2014 look better than the previous January-April 2013 period?).

As a result of my study of the structure and function of Google Analytics, I had learned how GA counts session data. Critical inquiries had questioned whether GA’s reporting of unique visits could be accurate given the browsing patterns of today’s web visitors. Visits (now sessions) are defined as individual browsing sessions on a given website on a given browser and platform. A visitor (now user) who visits the same website using two different browsers (Chrome and Firefox, for instance) would be calculated as two unique visits (when unique visits were provided) because the session is browser specific. Furthermore, a visitor who visits the same website on a desktop platform browser, then revisits the same website on a mobile device, would be calculated as two unique visits, because the session is platform specific. In short, “unique visit” is really a calculation of “individual session” without a distinction of uniqueness of the visitor. Using the term “unique visit” suggested (and my marketing team and I took it to mean) visits by unique users, a measurement we considered superior because it suggested the actual number of visitors. What we should have been measuring, however, was visits, regardless of their “uniqueness,” because there was no unique quality to the visit in terms of the visitor. The end result is that I will need to re-record our historical data in terms of sessions rather than unique visits, potentially revealing visit patterns we had not before seen or understood.

Without this study of GA as a network, I would not have understood why reporting data did not match, and I would have struggled to find documentation of the issue. There remains little documentation from Google itself about the disappearance of unique visit as a reported metric as of this date. In short, the application of my theoretical exploration directly benefited my and my team, and ultimately our school and our understanding of our data within the framework of industry benchmarks.

Theories of Networks and Google Analytics

I’m using two theories — Castells’ network society and Deleuze & Guattari’s rhizome — to flesh out my understanding of Google Analytics and sketch out my Frankentheory of a network.

First, here’s a review of some familiar territory: My application of Castells’ network society to GA from Case Study #2. I’ve brought this in as a piece rather than linking to it because I’d like to make departures from specific aspects of this application in discussing Deleuze & Guattari and in sketching out a Frankentheory.

Defining Google Analytics

Castells (2010) considers technology to be society (p. 5). As a result, 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 considered 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

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Google Data Center Locations: Image from Google Data Centers.

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 modeluser, 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

Framework for Movement: Wires in The Dalles, Oregon, Google Data Center. Photo from the Google Data Center Gallery.

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

Killing the Network: Failed Google data hard drives to be destroyed at the St. Ghislain, Belgium, Google Data Center. Photo from the Google Data Center Gallery.

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.

And Now, the Rhizome

First a note about using Deleuze & Guattari. I did not enjoy or particularly “get” this reading the first time around. I grasped the broad strokes of the argument, but this is a chapter that requires close, multiple readings. What I discovered as I re-read the chapter in light of this analysis was that it addresses a significant aspect of networks that Castells does not — namely, a rhizomatic approach to networks problematizes the very definition of GA I established during my Re/Proposal. In short, applying Castells profited from the boundaries I placed on the OoS; applying Deleuze & Guattari requires eliminating the boundaries, preferring instead a situated, chronological cross-section as a set of boundaries enabling analysis.

Second, a note about this cross-sectional approach. In my scaffolding outline, I referred to a “flattened, rhizomatic” approach to composing and networks. Placing these two concepts together elicited useful feedback and discussion during the following class, as a result of which I realized that rhizomes are not naturally flattened. While Deleuze & Guattari (1980/1987) refer to flattened multiplicities, they do so in the context of many dimensions: “All multiplicities are flat, in the sense that they fill or occupy all of their dimensions” (p. 9). In fact, rhizomes are unpredictably dimensional; connections can and must occur along all dimensions: “any point of a rhizome can be connected to anything other, and must be” (p. 7). Since the boundaries of such a “network” can’t really be established, one way to analyze the rhizome is to take a cross-sectional slice, situated in space and time, of the rhizome and examine the relationships among points in the rhizome in this “flattened” slice. The rhizome is a multidimensional assemblage, not a flattened network.

These two notes represent realizations that complicate and problematize the restrictive perspective I offered of GA as a network. Limiting the network to GA activities and data model resulted in limits to what I could discuss in my application of Castells. For example, in discussing the birth and death of the network, I cut short my analysis with this limiter: “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.” Similarly, when addressing nodal situation and relations, I wrote this limiting statement: “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.” These limits were real — the boundaries I established for describing GA as a network did, in fact, prevent addressing aspects of the network — but they do not reflect an accurate mapping of GA network activity. Deleuze & Guattari (1980/1987) point out that “the rhizome is altogether different, a map and not a tracing” (p. 12, emphasis original). Tracing is the role of centralized control, of perspectives limited by binaries and “tree logic”: “What distinguishes the map from the tracing is that it is entirely oriented toward an experimentation in contact with the real” (p. 12). A mapped understanding of GA must address its real complexity, its nodes and connections in terms of real experiences, not centrally-defined boundaries.

A mapped, cross-sectional perspective on GA as a network was, to my surprise, the goal of my first case study. In fact, the first visualization of the network I provided was a portion of a Popplet titled “Visualizing a Partial Google Analytics Data Set.”

Screen Shot 2014-05-06 at 8.03.11 PM

Figure 1: Visualizing a sample Google Analytics data set from Case Study #1Popplet

My original attempt to visualize and define GA as a network was more chaotically rhizomatic than any other depiction I’ve attempted since. In fact, for much of the rest of the semester, I’ve been struggling to trace my understanding of GA as a network, when in fact Deleuze & Guattari would have me do precisely the opposite: map the multiplicity of GA as assemblage, depicted as a cross-sectional portion of the network situated in time and space.

Mapping GA as rhizome means accepting that users, servers, computers, mobile devices, browsers, operating systems, marketers, developers, programmers, designers, GA account administrators, Google data centers, Google programmers and server maintenance personnel, homes, home offices, office buildings, network cables, routers, switches, weather conditions, satellites, trans-Atlantic communications cables, seawater, signal degradation, electrons, light energy, insulators, and theorists must be included as nodes in the GA rhizome. GA collects data on some of these dimensions; other dimensions, however, are embedded as affordances and constraints to the web technologies that enable GA to measure dimensions at all, so these affordances and constraints must also be depicted in a cross-section of GA as rhizome.

There’s a reason Deleuze & Guattari did not include a visualization of the rhizome on their chapter. It’s too complex, too multi-dimensional, to capture in a 2-dimension drawing. But I’m going to give it a shot.

Popplet mind map

Figure 2: Visualizing Google Analytics as a Rhizome—Popplet

Figure 2 depicts a rhizome cross-section of a single node, User, and the connections that exist among dimensions of the GA data model, website affordances and constraints, website creators, and Google personnel. What this depicts is that a User connects from and to most of the nodes, that the nodes connected to the User are connected to one another, and that relationships proliferate exponentially if extrapolated to the entire list of dimensions. And these dimensions are themselves necessarily limited (perhaps even cross-sectioned) by the visualization technology and my own time and patience. Were I to connect all of the non-technological aspects to the User—like location and weather conditions — the rhizome could go on forever. The point is that mapping the actual rhizome, rather than tracing the limits of the network, generates the rhizome itself. Or, as Deleuze & Guattari (1980/1987) propose, “The map does not reproduce an unconscious closed in upon itself; it constructs the unconscious. It fosters connections between fields, the removal of blockages on bodies without organs, the maximum opening of bodies without organs onto a place of consistency. It is itself a part of the rhizome” (p. 12).

Closing Gaps

Castells offers a remarkably cogent and highly matched means of analyzing GA as a network as defined by Google itself: in terms of GA activities and the GA data model. Castells addresses issues of localization and globalization in ways that make sense for GA defined as Google defines it. Here’s my conclusion from Case Study #2.

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).

Once we admit the possibility that GA is not just what Google says it is, but that GA represents a much wider and broader rhizome of connections, Castells no longer adequately describes the network. GA as rhizome requires additional theoretical application for understanding and visualizing.

Frankentheory

After a semester of theorizing, what’s my own theory of networks?

Rhizome illustration

What I think a rhizome looks like. “The Opte Project” by Barrett Lyon. Creative Commons licence CC BY-NC-SA. From The Accidental Technologist‘s post The Way of the Rhizome #h817open

Networks are local. They are also global. This is not dualism, but convergence. Local and global converge in time and space, and we must be prepared to engage in both simultaneously. The global remains rooted in the local; local conditions and environments affect and influence connections to the global. In our efforts to understand global network activity, we should not lose sight of the affordances and constraints of local conditions, including available access to the internet, proximity to other nodes, and the politics of nodal connectivity.

Networks enable nodes. A collection of nodes does not a network make. Networks enable nodal activity; this means that network frameworks must be in place for networks to exist and start collecting nods. This also means that the activity of collecting nodes in networked. The network can grow well beyond its framework in unexpected and unpredictable ways, and this should be expected, anticipated, and planned to the extent possible.

Networks are rhizomes. Or at least rhizomatic. They are unlikely to require or have inherent hierarchical structures; these will have to be applied to the network. Rhizomatic structure and growth suggest unpredictability of nodal connections. As I understand rhizomes, the importance of any node being able to connect to any other node — or to anything, for that matter — cannot be overstated. It is this aspect of rhizomatic connectivity that I would consider “flat.” There are neither more nor less important nodes; there are no inherent political relationships between and among nodes. Any political power attributed to the node will either be self-contained or bestowed from outside the rhizome; within the ecology of the rhizome, all nodes are equally capable of connecting to all other nodes and to anything outside the rhizome. In this sense, I would suggest that rhizomes are politically flat.

Networks can be analyzed in cross-section; they are very difficult to analyze in real time as they exist. They are both too large to examine as a whole and too complex to analyze as active connections are “firing.” Cross-sections can be taken of specific aspects of the network or of the network as a whole. Cross-sections are frozen in time and show little activity, merely traces that can be followed and explored. Networks contains a multiplicity of simultaneous connective activity; our abilities to analyze simultaneity is limited. Instead, we must follow specific threads of connectivity through time and space to analyze them. Such analysis is made possible through cross section.

Google Analytics’ Contributions to English Studies

First, GA can and should be critically examined as a rhetorical technology. GA activity includes reporting. These reports are discursive and rely on visual and written rhetoric to communicate meaning. The “meaning” of a GA report can be manipulated like any other statistical data. Its meanings depend on local environment and conditions, comfort with standard and local meanings of GA terminology (like “session” or “user,” for example), and familiarity with the GA data collection model. Its visualizations can be analyzed for clarity and transparency, for cultural or sociological bias (related to colors used, default views, and other determined factors), and for its connectedness to other discursive elements (like websites whose visitor traffic it measures). Critical rhetorical analysis of GA reports could easily be an object of study by itself.

Second, GA can and should be critically approached as a black-boxed network whose data manipulation and configuration are largely hidden, lacking transparency. Google’s business model depends on its proprietary search results algorithms. It protects that algorithm carefully; while GA reporting is not directly dependent on the search algorithm, website visit data contribute to search results. Full disclosure of its data configuration and processing activities would likely reveal much about Google’s search algorithm; as a result, these processes are only partially disclosed. Google’s own Analytics help files and tutorials explain the order, purpose, and general procedures of data configuration and processing, but these files and tutorials do not reveal in-depth specifics on how collected data are processed into aggregate tables, nor how those tables are then indexed for rapid, near-instant on-the-fly reporting. Google’s market share in web search and advertising result in the formation of what Althusser (1971) called a repressive state apparatus; I suggest that GA is an ideological expression of that apparatus, or an ideological state apparatus. While neither Google nor GA is a state in a political sense, its size and clout suggest an industrial state-like entity with resources and influence strong enough to manipulate or evoke responses from other political entities, as it has done recently in relations with the government of Russia (Khrennikov & Ustinova, 2014).

Third, GA results themselves can and should be critically examined. Far too many otherwise critically-written journal articles use GA results as instrumental rather than mediated. That is, GA report data are accepted as unqualified and accurate reflections of website traffic rather than mediated reports of visitor activity. Little care is given to providing GA-specific definitions of terminology like “session” and “user.” This acceptance can result in significant reporting issues — I’m experiencing a particular situation as I type in which Google has revised a reporting criterion from “visits” to “sessions.” While these two terms are being used synonymously, one implication is that GA has removed the dimension of “unique visit” from its reporting matrix. GA’s definition of session doesn’t differentiate between unique or repeat visits among sessions, as each session is considered a unique event regardless of the identity (which may not be accurately known) of the visitor. Several reports I provide my dean and marketing director were based on unique visit numbers; as a result, I’m forced to rework all of my reports to reflect sessions rather than unique visits. This has implications for perceptions of “progress” and “improvement” among senior leadership, a particularly uncomfortable reality brought to bear this week. (Google changed its reporting structure without fanfare on April 16, announced in a Google+ post.)

Finally, GA’s data collection method can and should be understood as discursive. Individual GIF calls that report data back to Google servers do so in text tags attached to tracking pixels generated through data collection. For example, every GA tag begins with “utm,” a prefix whose meaning is unclear. Many data points are collected in abbreviations whose symbolic meanings would be interesting to explore. Again, GA offers few clues for more obscure abbreviations, although Google does provide a list of many (but not all) dimensions collected via tracking pixel calls. Some of these symbols are explained in the Google Developers (2014) Tracking Code Overview. While parameter abbreviations are obscure, the values themselves are even less clear. Consider the parameter/value pair “utmul=pt-br”: the utmul parameter represents “browser language” while the pt-br value represents “Brazilian Portuguese.” This symbolic communication system is itself fodder for rhetorical analysis and interpretation.

References

Althusser, L. (1971). Ideology and ideological state apparatuses (Notes towards an investigation). In B. Brewster (transl.) & A. Blunden (trans.), Louis Althusser archive. Retrieved from https://www.marxists.org/reference/archive/althusser/1970/ideology.htm (Original work published in Lenin philosophy and other essays)

Castells, M. (2010). The rise of the network society [2nd edition with a new preface]. Chichester, UK: Wiley-Blackwell.

Deleuze, G., & Guattari, F. (1987). A thousand plateaus: Capitalism and schizophrenia. (B. Massumi, Trans.) Minneapolis, MN: University of Minnesota Press. (Original work published 1980)

Google. (n.d.). Algorithms. Inside Search. Retrieved from 1 May 2014 from https://www.google.com/insidesearch/howsearchworks/algorithms.html

Google. (2014). Dimensions and metrics. Google Analytics Help. Retrieved from https://support.google.com/analytics/answer/1033861?hl=en

Google Analytics. (2014, April 16). Understanding user behavior in a multi-device world (Web post). Google+. Retrieved 1 May 2014 from https://plus.google.com/+GoogleAnalytics/posts/LCLgkyCn4Zi

Google Developers. (2014, April 16). Tracking code overview. Google Developers. Retrieved from https://developers.google.com/analytics/resources/concepts/gaConceptsTrackingOverview#gifParameters

Krennikov, I., & Ustinova, A. (2014, May 1). Putin’s next invasion? The Russian web. Bloomberg Businessweek. Retrieved from http://www.businessweek.com/articles/2014-05-01/russia-moves-toward-china-style-internet-censorship

[ Feature image: Today's latte, Google Analytics. CC licensed image from Flickr user Yuko Honda ]

Plateaus, Networks, and the Mad Hatter Twins Take the Grand Stage

And, we’re back. But what to say about this week’s reading?

I guess, I should start by admitting that I probably should NOT have started with the excerpt from Guattari and Deleuze’s book,  A Thousand Plateaus: Capitalism and Schizophrenia. I loved Guattari’s Three Ecologies, but when I read the opening to their chapter “Introduction: Rhizome,” I was quite taken aback: “The two of us wrote Anti-Oedipus together. Since each of us was seven, there was already quite a crowd.” Maybe I’m just not up-to-speed with their understanding of math, but my first and second reactions were certainly:

Waving goodbye as we tumble down the rabbit hole. Image hosted on the blog, Pitter Patter.

Waving goodbye as we tumble down the rabbit hole. Image hosted on the blog, Pitter Patter.

We're All Mad Here. Image hosted on

We’re All Mad Here. Image hosted on Stack Exchange.

Alas, I digress before I have even begun. After my initial, “Woah” moment, I settled into reading. I particularly like Deleuze and Guattari’s conversations about books as taproots, “with its pivotal spine and surrounding roots,” going on in all different directions rather than just a dichotomy, but then move on to show that this is just as limiting as the dichotomous view that had been in place before. They offer a different image instead: “The radicle-system, or fascicular root, is the second figure of the book, to which our modernity pays willing allegiance. This time, the principal root has aborted, or its tip has been destroyed; an immediate, indefinite multiplicity of secondary roots grafts onto it and undergoes a flourish development. This time, natural reality is what aborts the principal root, but the root’s unity subsists, as past or yet to come, as possible” (5). And then comes their image of the rhizome, but an image that is not only limited to plants but certain animals as well (rats are their example) and, later, their comparison of rhizomes to books: “The rhizome is an anti-genealogy. The same applies to the book and the world: contrary to a deeply rooted belief, the book is not an image of the world. It forms a rhizome with the world, there is an aparallel evolution of the book and the world; the book assures the deterritorialization of the world, but the world effects a reterritorialization of the book, which in turn deterritorializes itself in the world (if it is capable, if it can). Mimicry is a very bad concept, since it relies on binary logic to describe phenomena of an entirely different nature” (11).

**Warning: The beginning of this video is a little weird as it has dramatic music playing through a textual/visual introduction. Once it ends, though, the video opens to an interview.

Side note: when Googling Deleuze and Guattari’s “radicle-system,” one of my image results was Christian Bale covered in blood from  his role in American Psycho. I did also find a blog entry on their Anti-Oedipus. One of the most interesting blog entries I have found employed Deleuze and Guattari’s concept of “collective assemblage of enunciation” into object-oriented rhetoric.

Gilles Deleuze and Felix Guattari. Image hosted on  website for Mike Hoolboom.

,  Gilles Deleuze and Felix Guattari. Image hosted on website for Mike Hoolboom.

A Break for Some Vocabulary

Rhizome – “a rootlike subterranean stem, commonly horizontal in position, that usually produces roots below and sends up shoots progressively from the upper surface” (Dictionary.com). But, the organic definition is not the only one. For Deleuze and Guattari, “A rhizome ceaselessly establishes connections between semiotic chains, organizations of power, and circumstances relative to the arts, sciences, and social struggles” (7).

Urban Photo Rhizome. Image hosted on blog, Bhopal 2011: Requiem & Revitalisation.

Urban Photo Rhizome. Image hosted on blog, Bhopal 2011: Requiem & Revitalisation.

Collective Assemblage of Enunciation – “focus on the manner in which impersonal statements are tied to collectives, and are not attributable to subjects…the subject is no longer divided in Cartesian sense between an enunciation (‘I think’) and a statement (‘I am’) that could constitute its being”  (The Deleuze and Guattari Dictionary by Eugene B. Young 70). These remind me of neurons in the brain that I had to read about with Neurobiology a few weeks ago.

Machinic Assemblages - “One side of a machinic assemblage faces the strata, which doubtless make it a kind of organism, or signing totality, or determination attributable to a subject; it also has a side facing a body without organs, which is continually dismantling the organism, causing asignifying particles or pure intensities to pass or circulate, and attributing to itself subjects that it leaves with nothing more than a name as the trace of an intensity” (Deleuze and Guattari 4)

Abstract Machine – “connects a language to the semantic and pragmatic contents of statements, to collective assemblages of enunciation, to a whole micropolitics of the social field” (Deleuze and Guattari 7)

Principle of Connection and Heterogeneity – “Any point of a rhizome can be connected to anything other, and must be. This is very different from the tree or root, which plots a point, fixes an order” (Deleuze and Guattari 7)

Principle of Multiplicity – “it is only when the multiple is effectively treated as a substantive, ‘multiplicity,’ that it ceases to have any relation to the One as subject or object, natural or spiritual reality, image and world. Multiplicities are rhizomatic , and expose aborescent psuedomultiplicities for what they are… A multiplicity has neither subject nor object, only determinations, magnitudes, and dimensions that cannot increase in number without the multiplicity changing in nature (the laws of combination therefore increase in number as the multiplicity grows” (Deleuze and Guattari 8)

Principle of Asignifying Rupture – “Against the oversignifying breaks separating structures or cutting across a single structure. A rhizome may be broken, shattered at a given spot, but it will start up again on one of its old lines, or on new lines…Every rhizome contains lines of sementarity according to which it is stratified, territorialized, organized, signified, attributed, etc., as well as lines of deterritorialization down which it constantly flees” (Deleuze and Guattari 9)

Principle of Cartography and Decalcomania – “a rhizome is not amenable to any structural or generative model. It is a stranger to any idea of genetic axis or deep structure. A genetic axis is like an objective pivotal unity upon which successive stages are organized; a deep structure is more like a base sequence that can be broken down into immediate constituents, while the unity of the product passes into another, transformational and subjective, dimension” (Delueze and Guattari 12)

After moving through their introduction, picking up vocabulary concepts along the way, I did find an image that helped me to visualize kind of where they were going with decoupling the tree root metaphor and moving towards rhizome. It was an adjustment to think of theories not as trees, branching outwards from the ground up, but as rhizomes branching out from wherever they can. This makes more sense, especially after doing the Theory Tree group work with some of my peers. We had encountered a problem, initially, with deciding how to shape the pathways of the authors, as they were drawing upon one another, crossing topics and subjects, looping back and adding outwards. Theory does not move in a linear fashion along a timeline, but links to other theories, even some that may be startling with their connections. In this way, Delueze and Guattari remind me of Foucault, in that they are tearing away at the image of the tree that had been so heavily embedded in how theorists saw their work moving into the network of theories playing out, but also looking at how those theorists (like Ninetieth) were undercutting what was seen as established: “Joyce’s words, accurately described as having ‘multiple roots,’ shatter the linear unity of the word, even of language, only to posit a cyclic unity of the sentence, text, or knowledge. Nietzsche’s aphorisms shatter the linear unity of knowledge, only to invoke the cyclic unity of the eternal return, present as the nonknown in thought” (Deleuze and Guattari 6). The shattering of unity of language and knowledge, sounds a lot like what Foucault was proposing with history and the history of ideas. There is no one unifying tree trunk because there is no linearity beyond that which we impose, but even that comes with selective inclusion and exclusion.

But, as soon as I start to follow Delueze and Guattari’s threads of thought, they produce sentences like, “Drunkenness as a triumphant irruption of the plant in us” (11). Seriously people, I think they are just messing with readers at that point. Or, maybe, in the haze of their LSD experiments, a statement like that (as well as the one where two people are seven) actually means something deep and awe-inspiring? Either way, such commentary leaves me brain-addled in the desert of rhizomatic confusion. Though, I must say, their declaration for readers to “Follow the plants” makes sense (I feel totally batty for having just written that) because plants find interesting ways of adapting to their surroundings and the climate and they branch off in anyway that will give them access to greater amounts of sunlight.

Someone's conceptualization of Delueze and Guattari's ideas. Image hosted on the website Lab404.

Someone’s conceptualization of Delueze and Guattari’s ideas. Image hosted on the website Lab404.

Rainie and Wellman Come Falling Down

Lee Rainie, Director of  Pew Research Center's Internet & American Life Project. Image hosted on the University of Maryland's website for the Human-Computer Interaction Lab.

Lee Rainie, Director of Pew Research Center‘s Internet & American Life Project. Image hosted on the University of Maryland’s website for the Human-Computer Interaction Lab.

Barry Wellman, professor at the University of Toronto. Image hosted on the website for the Workshop on Information in Networks.

Barry Wellman, professor at the University of Toronto. Image hosted on the website for the Workshop on Information in Networks.

Changing the metaphorical (and theoretical) gears, we turn to Lee Rainie and Barry Wellman’s book Networked: The New Social Operating System. This book was very different than our rhizome-obsessed dynamic duo, though the introduction tripped me up considerably as it was about a woman tripping and then having brain surgery. Rainie and Wellman’s focus turns towards individuals networking through technology: “When people walk down the street texting on their phones, they are obviously communicating. Yet things are different now. In incorporating gadgets into their lives, people have changed the ways they interact with each other. They have become networked as individuals, rather than embedded in groups. In the world of networked individuals, it is the person who is the focus: not the family, not the work unit, not the neighborhood, and not the social group” (6). It was interesting to read their ideas about how our interactions with communication technologies are reshaping the “social operating system” that they call “networked individualism” (6).

Distracted walking. Image hosted on Australian news site, SFGate, in the article "Tourist Walks Off Australia Pier While Checking Facebook."

Distracted walking. Image hosted on Australian news site, SFGate, in the article “Tourist Walks Off Australia Pier While Checking Facebook.”

It was a curious thing to think of people as part of an operating system, casting us in roles similar to the computers we make, buy, operate, upgrade, and love and hate. However, such an idea makes sense. Unless we are completely isolated, we function as nodes within groups at home, at work, at school, in places where we shop, eat, drink, relax. After looking at the picture above, those who are part of societies fluent in these communication technologies visually look like they are moving nodes in a network, always connected, exchanging information. We move through crowds of people on their phones, checking Facebook, Twitter, their emails, websites, Google Maps, and so on through the interwebs.

To push forth their idea of networked individualism, Rainie and Wellman list four aspects of the social network operating system: “personal–the individual is at the autonomous center just as she reaching out from her computer; multiuser–people are interacting with numerous diverse others; multitasking–people are doing several things; and multithreaded–they are doing them more or less simultaneously” (7). We’re all looking a little cyborg now. This reminds me of the articles I read on Cloud Computing at the beginning of the semester as we are part of the external framework of the global network, along with the computer hardware constantly at our fingertips. But Rainie and Wellman make a good point, one that resonates with Castells, that this social network operating system is founded on social networks that were already in place; it is not a new system (social groups already existed), but a newer system (one where proximity is not as important a detail anymore) that is enhanced by advancements in technology, giving us a broader reach and an ability to juggle more with (usually) efficiency. The authors are pushing back against people who warn against technology making us more isolated, finding that what people do with the technology is a constant reaching out rather than a drawing inward. However, they also found that people still want the physical connection and find that networking through communication technology is taxing in that they must constantly work at staying connected (sound like Latour with his observation that individuals in a group must constantly define and redefine the boundaries of their group. It takes work to be and stay connected.)

The more I think about it, the more I can see it both ways. Through my phone and computer, I can be in constant contact with someone, anyone, and yet, by being on my phone while I am physically near someone, I am (in a sense) constructing a mental wall against that person. This reminds me of family dinners where I would be sitting next to my mother, stepfather, and younger sister, only to have no one speaking. My mother and stepfather would be playing word games with each other, but messaging through the text feature, and my little sister would be texting her friends or following her favorite celebrity (Justin Bieber, at that time). The same thing happens all the time in restaurants, on buses or light rails, or even just walking down the street. The people around us can become physical shadows we don’t pay much attention to because there are people with whom we can connect virtually who more clearly share our interests, are friends from back home, or are family members who can now be reached without using the seemingly obsolete snail mail.

 

How to escape a social function like a ninja? Phone call. Image hosted on a website for House of Cards.

How to escape a social function like a ninja? Phone call. Image hosted on a blog for House of Cards.

Rainie and Wellman discuss three revolutions that have taken place as communication technologies shape how we interact with others: 1) “The Social Network Revolution has provided opportunities–and stresses– for people to reach beyond the world of tight groups” (it’s no longer enough to be an isolated tribe. Need to link outwards); 2) “the Internet Revolution has given people communication powers and information-gathering capacities that dwarf those of the past” (which can sometimes result in this); 3) “the Mobile Revolution has allowed ICTs [information and communication technology] to become bodily appendages allowing people to access friends and information at will, wherever they go” (11-12).

Analytical Scott Joins in the Chorus

[add more here]

John Scott's figures of compiling a Sociogram, on page 45.

John Scott’s figures of compiling a Sociogram, on page 45.

And so ends our story of Rhizomes, Networked Individuals, and Sociograms. Just for a while, loves. These things always crop back up.

Enough internet for Dean Winchester, from Supernatural.

Enough internet for Dean Winchester, from Supernatural.

References

Deleuze, Gilles and Felix Guattari. A Thousand Plateaus: Capitalism and Schizophrenia. Trans. Brian Massumi. Minneapolis: University of Minnesota Press, 1987. [PDF].

Raine, Lee and Barry Wellman. Networked: The New Social Operating System. Cambridge, MA: MIT Press, 2012. [PDF].

Scott, John. Social Network Analysis: A Handbook. 2nd Ed. Los Angeles: Sage, 2010. [PDF].

Long Live the Multiple


Reading Notes: Deleuze & Guattari, Scott, and Rainie & Wellman

Deleuze & Guattari

Reading A Thousand Plateaus: Capitalism and Schizophrenia by Deleuze and Guattari (1980/1987) is an experience in disorientation and reorientation. The first pages are entirely disorienting. What is that scribbled piano piece for David Tudor? What is a book without subject or object? How are lines and measurable speed related to the assemblage, and is an assemblage the same as a multiplicity? (pp. 3-4). Truth be told, I don’t think I can effectively answer those questions even after reading the chapter! But intrepid readers will eventually right themselves from their disorientated states, as I did, and discover that Deleuze and Guattari are seeking to break readers from their habitual arboreal metaphorical existence. And this does not mean emerging from the trees. It means engaging in flattened, networked, metaphorically rhizomatic thinking rather than hierarchical, binary, linear, metaphorical tree/branch thinking (p. 17). It means embracing the pragmatic schizophrenia of lived experience in all its networked, nonlinear glory rather than idealized linearity that doesn’t really exist in the lived world.

Tufte essay cover

The Cognitive Style of PowerPoint” – Tufte’s critique of arborescent thinking from EdwardTufte.com

I connected this chapter to ideas in Edward R. Tufte’s (2006) essay “The Cognitive Style of PowerPoint: Pitching Out Corrupts Within.” Tufte’s (n.d.) critique of PowerPoint as a technology that “usually weakens verbal and spatial reasoning, and almost always corrupt statistical analysis” reflects the point Deleuze and Guattari (1981/1987) make in this chapter: that linear thinking in root/tree/branch structures (like PowerPoint’s points and outlines) simply traces and reproduces rather than analyzing and imagining (p.12). Rhizomatic thinking, on the other hand, maps with creativity and imagination in connections that can’t be predicted or controlled (p. 12). In the same way that PowerPoint stifles analysis and reasons, arborescent thinking stifles imagination, creativity, and connection. Deleuze and Guattari point to the perpetual “interbeing” of the rhizome as the alternative, or at least the preferred complement, to the arborescent metaphor for thought (p. 25).

Rainie & Wellman

What Deleuze and Guattari theorize, Rainie and Wellman explain. Accepting the rhizomatic character of 21st century networked individuals as the norm, Rainie and Wellman (2012) seek to describe the environmental and social affordances that enable networked individuality. They settle on describing networked individualism as an “operating system” to reflect that “societies — like computer systems — have networked structures that provides opportunities and constraints, rules and procedures” (p. 7). They continue to define the social network operating systems as personal, multiuser, multitasking, and multithreading (p. 7). These characteristics of the network operating system reflect the rhizomatic character of networked thinking and theorizing that Deleuze and Guattari theorize. These characteristics also point to the influence of Latour’s (2005) emphasis on the individual node as the center of the activity network, to Castells’ (2010) claim that society is a “space of flows” (to which Rainie and Wellman make direct reference, p. 102), and to Scott’s reference to the emerging schism in social network theory between those, like Homans, seeking to build a social theory around small-scale social interaction and others, like Parsons, who sought to build social theory around larger social networks (p. 23).

Rainie and Wellman (2012) identify the “Triple Revolution” of Social Network, Internet, and Mobile Revolutions “coming together to shift people’s social lives away from densely knit family, neighborhood, and group relationships toward more far-flung, less tight, more diverse personal networks” (p. 11). I took these three revolutions to represent environmental affordances enabling the development of networked individualism. In Chapter 4, which I was assigned, Rainie and Wellman address the contributions to networked individualism afforded by the explosive availability and implementation of mobile and wireless technologies: “Mobile phones have become key affordances for networked individuals as they have become easier to carry, cheaper to use, and able to function in more places” (p. 84). The ability to function in more places has become increasingly important in developing nations, where hardwired infrastructure is impractical and often skipped over on favor of cheaper wireless technology. As a result, “by 2011, more than three-quarters of the world’s mobile phones were in less-developed countries, with China alone having some 879 million subscribers” (p. 89). This represents the reduction of the digital divide among mobile phone users, a significant milestone toward which “teens are showing the way” (p. 87). The result of mobile affordances is that place becomes both less and more important. While it’s true that “the closer that people live and work together, the more contact they have” (p. 101), it’s also true that space and time are becoming “softer” and “distance is not dead, it is just being renegotiated…. Your place is where your connectivity is” (p. 108).

Rhizome visualization

“The rhizome is altogether different, a map and not a tracing” (Deleuze & Guattari, 1980/1987, p. 12). Image from Hypertext ICAM130 SP06

Place as connectivity” echoes Castells’ “space of flows” and Latour’s flattened localized nodes. It also reflects the rhizomatic society that Deleuze and Guattari theorize, in which Rainie and Wellman’s (2012) “continuous partial attention” (p. 108) and “present absence” (p. 103), Campbell and Park’s “connected presence,” and Gergen’s “absent presence” (qtd. in Wellman & Rainie, 2012) can all feel perfectly comfortable, a space of connection and heterogeneity: “any point of a a rhizome can be connected to anything other, and must be” (Deleuze & Guattari, p. 7).

Oxymoronic phrases? Only in arborescent metaphorical thought.

References

Deleuze, G., & Guattari, F. (1987). A thousand plateaus: Capitalism and schizophrenia. (B. Massumi, Trans.) Minneapolis, MN: University of Minnesota Press. (Original work published 1980)

Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford, UK: Oxford University Press. Clarendon Lectures in Management Studies

Rainie, L., & Wellman, B. (2012). Networked: The new social operating system. Cambridge, MA: MIT Press.

Scott, J. (2000). Social network analysis: A handbook (2nd ed.). Los Angeles, CA: Sage.

Tufte, E. R. (n.d.). Essay: The cognitive style of PowerPoint: Pitching out corrupts within [Webpage summary]. Retrieved from http://www.edwardtufte.com/tufte/powerpoint

Tufte, E. R. (2006). The cognitive style of PowerPoint: Pitching out corrupts within (2nd ed.). Cheshire, CT: Graphics Press.

[ Featured ImageAlaska forest - trees. More rhizomatic than Deleuze and Guattari make them out to be? CC licensed image from Flickr user Barbara. ]