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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newspapers to discover that the “unique visits” metric that I had been using was no longer available; instead, it had been replaced by the “sessions” metric, without the “unique” modifier.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.”

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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 ]

Reading Notes: Week 14

Summary

In their introduction to A thousand plateaus: Capitalism and schizophrenia, Deleuze and Guattari examines the use of trees and roots as metaphors for the structure of existence. While focusing primarily on texts and writing for their analysis, their argument is that the rhizome is a better metaphor for networks. Because of its ability to assume “diverse forms” (p. 7), the rhizome better describes the relationships between nodes than does the tree–which forces a structure that contains both a beginning and an end. Additionally, Deleuze and Guattari offer  characteristics of rhizomes to support their argument:

  1. Connection and heterogeneity: “any point of the rhizome can be connected to anything other, and must be” (p. 7). The unending connections between “semiotic chains, organizations of power, and circumstances relevant to the arts, sciences, and social struggles” allow for better representation of the limitless connections from which objects emerge.
  2. Mulitplicity: “it is only when the multiple is treated as substantive . . . that it ceases to have any relation to the One as subject or object, natural or spiritual reality, image and world” (p. 8). Unlike the tree metaphor, which relies on one structure that is reproduced, the rhizome has no unity that can be divided. There are no points or positions–only lines.
  3. Asignifying rupture: “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” (p. 9). Stratifications, lines of flight, and regenerations form a horizontal, non-linear structure of organization.
  4. Cartography and decalcomania: “a rhizome is not amenable to any structural or generative model”–“a map and not a tracing” (p. 12). Unlike a tree, which is a structure by which we try to mold the tracing of other structures, a rhizome’s structure provides a map of connections. “The map is open and connectable to

These characteristics, they argue, represent the book. A book is not an image but an overlapping of ideas and contributions from other people, ideas, books, and values.

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. ]

Mind Map April 6th: Neurons and Synapses

This week’s mind map began with an image choice that was — excuse me — a “no brainer” (you’re welcome, punsters; and I’m sorry, punsters). The image of the neuron — with all its accompanying transmission components — seemed well … Continue reading

Mind Map: Week 11

Mindmap11

In my mind map this week, I added a primary node of Ecology with smaller nodes linking out to Spellman and Syverston (since my book didn’t come until Tuesday, I only read summaries and, thus, I need to read him before I try to add him). As a result of our discussions in class this week, I created a connecting contrast node between Spellman and Latour. I actually had a date with a biologist on Saturday who studies freshwater streams and lakes, so this was a topic of our conversation. It was interesting for me to try to explain my perception (based on our readings and discussions) that ecology focuses on groups and classification. He didn’t see it until I explained how Latour’s theory of tracing all of the messy connections to an individual helps to define that individual’s network–the result of which would not be generalizable to other individuals. For instance, a species of fish serves a role in an ecosystem–its niche–and the role could be filled by any other of the fish in that species. However, while human individuals also serve a role in their network, all of an individual’s roles within his or her own specialized network cannot be fulfilled by another individual, because we have such a high level of agency and the importance we place on social systems.

I also added a primary node for Syverston and connected her concept of emergence to Bazerman, as I see a direct connection with the concept of speech acts and genres. This is a connection I plan to explore more as part of my own research.


Mind Map: Ecologies Part II (March 30th)

Link: http://popplet.com/app/#/1571354 Last week’s activities asked us to apply our network questions to the Ecology readings of Syverson, Spellman, the Cary Institute, and fill in the gaps with Guattari, resulting in new connections for my mind map. And even though … Continue reading

Mind Map #10: Seeking Homeostasis

Popplet mindmap visualization

Mindmap #10: Seeking Homeostasis (Popplet visualization)

The ecology of my mind map seeks homeostasis, a natural balance among its many theories. My mind map has become, in Charles Darwin’s words, a “web of complex relations” (cited in Spellman, 2007, p. 4).  Well, maybe not as complex as all of nature, but if we follow the formula for the value of a network from Castells (2000) — “the value of a network increases as the square if the number of nodes in the net” (p. 71), expressed as V=n(n-1), where n is the number of nodes in the network — then we’re looking at a pretty significant number of potential connectivities among all these theories. That’s pretty complex. (I had to check: the number of nodes related specifically to theories in the mind map is around 75 right now, so V=7574. That’s higher than any calculator I have access to can count.)

I linked the three ecologies from Guattari to my ecology node. I used Spellman’s (2007) focus on homeostasis (p.15) as a node as well, connecting it to the both the relationship between the organism and the environment (an important aspect of the definition of ecology) and the relationship between Guatarri’s three ecologies. Both Spellman and Guattari invoke the importance of seeking an equilibrium within ecologies or biosphere. Since “it is people through their complex activities who tend to disrupt natural controls” (Spellman 2007, p. 15), achieving homeostasis in ecosystems in which humans are active participants is incredibly difficult.

I focused specifically on the relationship between environment and organism as the focus of homeostasis, but I also added distributed intelligence as a node related to all aspects of the network of ecology. Distributed intelligence, cognition, value — whatever the term we wish to use — is becoming an important, common theme among several theorists. Our theorists are no longer willing to propose meaning be found in a single aspect of a networked environment; on the contrary, value has been placed in the interrelationships among network nodes. If I had to define what I consider a network right now, I’d probably focus on distributed value among actively connected nodes. Individual nodes may be valuable, but in the network system, the value of an individual node is found in its contributions to the distributed meaning or value of the network itself. And that distributed meaning gains value only in its active state; in a passive state in which connections are theorized but not activated, the nodes provide only a framework for potential connectivity, distribution, and meaning or value.

I’m not sure how to convey all this in a mind map yet, but I expect I may center and enlarge “Distributed Intelligence” and start connecting many different mind map nodes to that important concept as I move forward. Castells shows so signs of moving away from this model of distributed meaning and value. And maybe it’s in emphasizing this distribution that my mind map will find the homeostatic condition it seeks (or maybe I’m on the one seeking it).

References

Guattari, F. (2012/1989). The three ecologies. Trans. Ian Pindar & Paul Sutton. London, UK: Continuum International Publishing Group.

Spellman, F. R. (2007). Ecology for nonecologists. Lanham, MD: Government Institutes, 3-23; 61-84.

[Top image – Narrative Ecology Framework flashcards: CC licensed image from Flickr user Crystal Campbell]

Mind Map #10: Seeking Homeostasis

Popplet mindmap visualization

Mindmap #10: Seeking Homeostasis (Popplet visualization)

The ecology of my mind map seeks homeostasis, a natural balance among its many theories. My mind map has become, in Charles Darwin’s words, a “web of complex relations” (cited in Spellman, 2007, p. 4).  Well, maybe not as complex as all of nature, but if we follow the formula for the value of a network from Castells (2000) — “the value of a network increases as the square if the number of nodes in the net” (p. 71), expressed as V=n(n-1), where n is the number of nodes in the network — then we’re looking at a pretty significant number of potential connectivities among all these theories. That’s pretty complex. (I had to check: the number of nodes related specifically to theories in the mind map is around 75 right now, so V=7574. That’s higher than any calculator I have access to can count.)

I linked the three ecologies from Guattari to my ecology node. I used Spellman’s (2007) focus on homeostasis (p.15) as a node as well, connecting it to the both the relationship between the organism and the environment (an important aspect of the definition of ecology) and the relationship between Guatarri’s three ecologies. Both Spellman and Guattari invoke the importance of seeking an equilibrium within ecologies or biosphere. Since “it is people through their complex activities who tend to disrupt natural controls” (Spellman 2007, p. 15), achieving homeostasis in ecosystems in which humans are active participants is incredibly difficult.

I focused specifically on the relationship between environment and organism as the focus of homeostasis, but I also added distributed intelligence as a node related to all aspects of the network of ecology. Distributed intelligence, cognition, value — whatever the term we wish to use — is becoming an important, common theme among several theorists. Our theorists are no longer willing to propose meaning be found in a single aspect of a networked environment; on the contrary, value has been placed in the interrelationships among network nodes. If I had to define what I consider a network right now, I’d probably focus on distributed value among actively connected nodes. Individual nodes may be valuable, but in the network system, the value of an individual node is found in its contributions to the distributed meaning or value of the network itself. And that distributed meaning gains value only in its active state; in a passive state in which connections are theorized but not activated, the nodes provide only a framework for potential connectivity, distribution, and meaning or value.

I’m not sure how to convey all this in a mind map yet, but I expect I may center and enlarge “Distributed Intelligence” and start connecting many different mind map nodes to that important concept as I move forward. Castells shows so signs of moving away from this model of distributed meaning and value. And maybe it’s in emphasizing this distribution that my mind map will find the homeostatic condition it seeks (or maybe I’m on the one seeking it).

References

Guattari, F. (2012/1989). The three ecologies. Trans. Ian Pindar & Paul Sutton. London, UK: Continuum International Publishing Group.

Spellman, F. R. (2007). Ecology for nonecologists. Lanham, MD: Government Institutes, 3-23; 61-84.

[Top image – Narrative Ecology Framework flashcards: CC licensed image from Flickr user Crystal Campbell]

Week 11: Ecological Systems

Summary

This week’s readings all center on the complexity of ecological systems. In chapter one of his book, Spellman attempts to define ecology and its importance in nonecologist terms. First, he traces various definitions of ecology, including the most widely used: “the scientific study of the distribution and abundance of living organisms and how the distribution and abundance are affected by the interactions between the organisms and their environment” (pp. 3-4). The definition he offers for his text, however, focuses more heavily on the relations: “Ecology is the science that deals with the specific interactions that exist between organisms and their living and nonliving environments” (p. 5). Like the Cary Institute’s definition (“The scientific study of the processes influencing the distribution and abundance of organisms, the interactions among organisms, and the interactions between organisms and the transformation and flux of energy and matter”), Spellman’s primary emphasis is on interactions and how those interactions maintain or transform an ecosystem.

Spellman explains that an organism’s ecosystem can be divided into four parts: Habitat and distribution, other organism, food, and weather. Additionally, there are four main subdivisions of ecology:

  1. Behavioral
  2. Population
  3. Community
  4. Ecosystem

He also emphasizes several key points regarding ecology. First, he asserts that no ecosystem can be analyzed in isolation( (p. 4). Next, he explains that ecology is typically categorized according to complexity (p. 5), which results in levels of organization (p.14):

Organs–>Organism–>Population–>Communities–>Ecosystem–>Biosphere

Finally, he emphasizes that each organism in an ecosystem has a specific role, or niche, to fill and “in order for the ecosystem to exist, a dynamic balance must be maintained among all biotic and abiotic factors–a concept known as homeostasis” (p. 15). The rest of chapter one explains how the different elements of an ecosystem all form a network of interrelated components that work together to maintain the ecological balance. He further identifies that energy moves through the system that operates as a cyclic mechanism (p. 17).

In chapter four, Spellman narrows his discussion to focus specifically on population ecology, defining a population system as “a population with its effective environment” (p. 62).  He identifies four major components of a population system: the population itself, resources, enemies, and environment (p. 62). Spellman identifies key principles and mathematic formulas for understanding population growth and reduction, highlighting the importance of ecological equivalency (p. 62) and the properties of populations (pp. 63-64):

  1. Population size
  2. Population density
  3. Patterns of dispersion
  4. Demographics
  5. Population growth
  6. Limits on population

 

From Mr G's Environmental Systems: http://sciencebitz.com/?page_id=41

From Mr G’s Environmental Systems: http://sciencebitz.com/?page_id=41

While he spends a lot of time on the laws of population ecology, the key factors of this chapter in terms of our networks class are his explanations of how limits (and lack of limits) affect population growth, his proposed methods for studying populations, and his explanation of how distribution occurs in an ecosystem. When all populations in a a given ecosystem are in balance, he explains, the ecosystem is balanced. Ecological succession is a key component of ecological balance. Succession allows an ecosystem to “heal” itself once the unbalancing factors are removed or overcome.

 

This concern for ecological balance seems to be at the forefront of Guattari‘s argument (my book has been on backorder and should arrive tomorrow, so I’ve read several summaries in the meantime). Guattari seems concerned with what we can understand about existence as a society rather than simply an objective observation of interrelations: “Ecology in my sense questions the whole of subjectivity and capitalistic power formations” (qtd. in Sytaffel). Sytaffel explains Guattari’s argument for an ecosophy that contrasts “a capitalist system predicated on economic growth.”

Finally, Syverson expands the theory of ecology from its environmental situation to a metaphorical application to writing, proposing an “Ecology of Composition.” Current theories of writing, she argues, do not account for the complex systems of writesr, texts, and audiences. Complex systems, she explains, are simultaneously spontaneous, self-organizing, adaptive, dynamic, unpredictable, disordered, and structured, coherent, and purposeful, they better reflect the network of agents that constitute the act of writing. This approach, Syverson explains, “takes into account the complex interrelationships in which the writing is embedded” (p. 6). To fully explain the metaphor, Syverson highlights key concepts from ecology and applies them to composition: distribution, emergence, embodiment, and enaction.

Syverson then breaks the theory down into the dimensions of complex systems to better illustrate “how the attributes of distribution, emergence, embodiment, and enaction can be revealed in ecologies of composing” (p. 18):

  1. Physical-material (including technology): “Texts emerge through writers’ and readers’ physical interactions with material structures” (p. 18)
  2. Social (inter-individual): encompasses not only the interactions between individuals but also social structures, practices, and relationships (p. 19_
  3. Psychological (intra-individual): the thoughts, emotions, neurophysiology of attention, language recognition, and text comprehension involved in writing (pp. 19-20)
  4. Spatial: Texts are composed across both bounded and unbounded spaces (p. 20)
  5. Temporal: discourse is historically and culturally situated (pp. 20-21)

Syverson concludes by emphasizing the need for a comprehensive theory of composing, pointing out that current theories neglect different dimensions revealed through this ecological metaphor.

Discussion

Spellman’s discussion of population diversity  and succession and Guattari’s emphasis on the human capitalist impact on the environment, made me think of the History Channel’s show, Life after People. In this clip form the episode on Chernobyl, we see how quickly the environment begins to heal itself when devastated then abandoned by humans:

The discussions of ecology have been interesting for me (especially Syverson) in that they have provided me a new way of considering new media composing. In her chapter, Syverson discusses the concept of emergence, which seems to tie with earlier conversations regarding genre and discourse. She claims that “to get a comprehensive understanding of composition, we need to understand how distribution , emergence, and embodiment are enacted through activities and practices in composing situations” (p. 13). Combined with Gibson’s theory of affordances and Norman’s theory of perceived affordances, this approach seems useful in beginning to understand the act of composing new media. As a result, I’m now going to look at adding this approach to my reading list.

 

Key Terms

Abiotic Factor: nonliving or inorganic substances such as oxygen and carbon dioxide

Biotic Factor: the living part of the environment composed of organisms that share the same area, are mutually sustaining, interdependent, and constantly fixing, utilizing, and dissipating energy (Spellman, p. 20)

Carrying Capacity: the optimum number of species’ individuals that can survive in a specific area over time (Spellman, quoting Enger, Kormelink, Smith, and Smith, 1989, p. 70). Two types: ultimate and environmental

Community: includes all of the populations occupying a specific area (Spellman, p. 20)

Complex Systems: a network in which independent agents act and interact parallel with each other, simultaneously reacting to and co-constructing their own environment (Syverson, p. 3)

Density-dependent Factors: the effect of the factor on the size of the population depends upon the original density or size of the population

Density-independent Factors: ones where the effect of the factor on the size of the population is independent and does not depend upon the original density or size of the population (p. 68)

Distribution: processes–including cognitive processes–are distributed; both divided and shared among agents and structures in the environment (Syverson, p. 7)

Ecological Succession: the observed process of change in the species structure of an ecological community over time; a gradual and orderly replacement of plant and animal species that takes place in a particular area over time (Spellman, p. 76)

Ecosystem: the community and the nonliving environment functioning together as en ecological system (Spellman, p. 20)

Egress: emigration or departure of an organism from a population (Spellman, p. 67)

Embodiment: the content and process of interactions are dependent on and reflective of physical experience (Syverson, p. 12)

Emergence: the self-organization arising globally in networks of simple components connected to each other and operating locally (Syverson, p. 11)

Enaction: the principle that knowledge is the result of an ongoing interpretation that emerges through activities and experiences situated in specific environments (Syverson, p. 13)

Homeostasis: a natural occurrence during which an individual population or an entire ecosystem regulates itself against negative factors and maintains an overall stable condition (Spellman, p. 20)

Ingress: immigration or arrival of a new organism to a population from other places (Spellman, p. 67)

Mortality: death rate (Spellman, p. 67)

Natality: birth rate (Spellpam, p. 67)

Species Diversity: a measure of the number of species and their relative abundance (Spellman, p. 75)

References

Spellman, F. R. (2008). Ecology for nonecologists. Lanham, MD: Government Institutes.

Syataffel. (Oct. 7, 2008). The three ecologies–Felix Guattari. Media ecologies and digital activism. Retrieved from http://mediaecologies.wordpress.com/2008/10/07/the-three-ecologies-felix-guattari/

Syverston, M. A. (1999). Introduction: What is an ecology of composition? The wealth of reality: An ecology of composition. Carbondale: S Illinois University Press.


Reading Notes: Ecology and Distributed Composing

I plowed through all of these readings in a single sitting, which did not help much with comprehension, but which did affect my experience reading. I’m a composition teacher who prefers to grade all assignments in a single sitting, an web manager who prefers to make changes that affect multiple parts of the navigation structure in a single work session, and a reader who, honestly, prefers to read at least an entire “piece” of writing in a single reading session. What I discovered as I read these in a single sitting is that the works built off one another in interesting ways. I’ll admit to being puzzled by Guattari; it’s always troubling when the translators’ introduction is clearer than the text itself! But I found that, once I started writing myself into a summary, I began to better understand what I had read (no surprise there, of course). More significantly, I found elements of Guattari’s (2012/1989) focus on the interrelationships and interplay among mental, social, and environmental ecologies informative in understanding the relationship of abiotic processes and elements in a natural or agroecology (Spellman, 2007). And Guattari’s insistence that we humans must be engaged in addressing and recovering our environmental ecological equilibrium offered a useful insight into ecological homeostatasis. Finally, Guattari, Spellman, Bateson (1987/1972), and Gibson (1986/1979) were important background readings that made Syverson (1999) accessible and understandable.

Dynamic Interrelationships

Repeated emphasis on the dynamic relationships within ecology was not lost on me. Guattari (2012/1989) refers to “the tangled paths of the three ecological vision” (p. 44) while the Cary Institute of Ecosystem Studies (2014) notes that ecology is about relationships, an “encompassing and synthetic” rather than “fragmented” view of nature. Spellman (2007) writes that “ecology is all about interrelationships, infraspecific and interspecific, and how important it is to maintain those relationships—to ensure our very survival” (p. 6). And Syverson (1999) draws on all these sources and more to define ecology as it relates to composition as “An ecology is a kind of meta-complex system composed of interrelated and interdependent complex systems and their environmental structures and processes” (p. 5).

These references reinforce my emerging understanding of networks as connected, interrelated, and active. I’m being drawn to articulate networks as requiring dynamic connections. Without active interrelations, the only aspect of the network that exists is a framework and/or a trace. But when the activity occurs — and the activity may take only a split second to occur — in that moment the network emerges. Ecologies are active networks. They don’t exist merely as frameworks or traces, although they may develop their own frameworks and hierarchies, like those described by Spellman (2007, quoting Odum 1983): “the best way to delimit modern ecology is to consider the concept of levels of organization” (p. 14). They can certainly be traced, as evidence my Spellman’s multiple examples of tracing ecosystems and populations within ecosystems.

Composing Ecologies

Word map

Mapping a composition ecology: WordItOut CC licensed image

I’ve thought a great deal this semester about the way I teach composition. I’ve radically altered my instruction this semester to reflect my nascent understanding of current composition theory. I’ve spent much of the semester convincing my students that composing is an experience that encompasses much more than the rhetorical triangle. Syverson offers a much clearer way to articulate what I think I’ve been hinting at all semester:

“I would argue that writers, readers, and texts form just such a complex system of self-organizing, adaptive, and dynamic interactions. But even beyond this level of complexity, they are actually situated in an ecology, a larger system that includes environmental structures [see the Ecology Map of My Classroom]… as well as other complex systems operating at various levels of scale…” (p. 5)

I’m encouraging my students to problematize their own understanding of the writing process as a class experience, as a social experience, and, I can now claim, as a complex activity system in an ecology. I believe doing so positions them to succeed in the real-world writing they will be asked to do beyond my classroom, academic, professional, and personal. And I also believe that doing any less robs them of an understanding of composing that makes too many assumptions about what writing is and is not.

Tricksters on the Edge

Anansi the Spider image

Anansi the Spider: A trickster who weaves a web of deceptions, but also creates a subversive narrative.

Once again I somewhat reluctantly admit trickster back into my understanding of Syverson specifically, and ecologies more generally. Syverson (1999) writes that “complex systems are dynamic, more unpredictable, spontaneous, and disorderly than a machine” (p. 4) and “the frontiers of knowledge are found… in the articulations between levels of organization of the real that correspond to different fields of knowledge whose techniques and discourses do not overlap” (p. 3). The dynamic tension that affords complex systems is precisely the dynamic tension of interstices and boundary spaces that trickster inhabits. Trickster inhabits liminal spaces and takes advantage of boundaries as crossing spaces and times. Boundaries are spaces in which connections can and do occur, the results of dynamic tensions at articulation points. And that is one way to describe the environment of tricksters and the affordance of ecology. If ecology is about interrelationships, then boundaries contain the connective tissues that afford ecologies. And where boundaries happen, tricksters emerge, sometimes releasing raw sewage into the stream, the immediate result of which is destruction, but the long-term result of which might ultimately be Spellman’s (2007) ecology succession, the method by which an ecosystem either forms or heals itself (p. 82). And sometimes the trickster creates something new from the dynamic interrelationships, like a genetically modified organism.

Trickster generally creates to destroy and destroys to create, unpredictably and selfishly. Sounds a little like a problem to which Guattari’s (2012/1989) focus in The Three Ecologies seeks a remedy.

References

Bateson, G. (1987/1972). Steps to an ecology of mind: Collected essays in anthropology, psychiatry, evolution, and epistemology. Northvale, NJ: Jason Aronson, Inc.

Gibson, J. J. (1986/1979). The ecological approach to visual perception. Hillsdale, NJ: Lawrence Erlbaum Associates.

Guattari, F. (2012/1989). The three ecologies. Trans. Ian Pindar & Paul Sutton. London, UK: Continuum International Publishing Group.

Spellman, F. R. (2007). Ecology for nonecologists. Lanham, MD: Government Institutes, 3-23; 61-84.

Syverson, M. A. (1999). The wealth of reality: An ecology of composition. Carbondale, IL: Southern Illinois University Press, 1-27.

[Featured image - Distributed composition: CC licensed image by Flickr user Gexydaf]

Reading Notes #9: Ecology Systems

This week’s readings on ecologies was a bit overwhelming. Guattari’s argument is an advancement of Bateson’s Steps to an Ecology of Mind. I was delighted to find that Guattari’s The Three Ecologies was much shorter than I thought. However, this delight shifted to sadness upon realizing that Guattari’s work centered on the deterioration of human […]