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Mindmap #11: The Network Society

Castells represented 500 page of network goodness, and I savored (quickly) every morsel. I struggled to limit what I planned to include in this week’s mindmap, settling on a tried and true method: I use the table of contents to organize my new nodes.

Popplet mindmap visualization

Mindmap #11: The Network Society. Adding in Castells’ The Network Society (Popplet).

I linked Castells to Foucault, Latour, and aspects of ecology.

I found Castells’ depiction of the network enterprise as a virtual culture similar to Foucault’s (2010/1972) desire to “restore to the statement the specificity of its occurrence… it emerges in its historical irruption” (p. 28). Castells (2010) writes about the network enterprise that it “learns to live within this virtual culture. Any attempt at crystallizing the position in the network as a cultural code in a particular time and space sentences the network to obscelesence, since it becomes too rigid for the variable geometry required by informationalism” (p. 215).

I found Castells’ description of mega-cities quite similar to Latour’s description of individuation through increasing nodal connections. Latour (2005) writes about the emergence of the actor-network, “it is by multiplying the connections with the outside that there is some chance to grasp how the ‘inside’ is being furnished. You need to subscribe to a lot of subjectifiers to become a subject ad you need to download a lot of individualizers to become an individual — just as you need to hook up a lot of localizers to have a local place and a lot of oligoptica for a context to ‘dominate’ over some other sites” (p. 215-6). Castell’s identifies three characteristics of mega-cities in the space of flows, the third being “connecting points to the global networks of every kind; the Internet cannot bypass mega-cities: it depends on the telecommunications and on the ‘telecommunicators’ located in those centers” (p. 440).

And I found Castells’ closing statements about social action similar to a couple of our definitions of ecology, especially to Spellman’s focus on the relationship of the organism to the environment. Spellman (2007) writes that “ecology is the study of the relation of an organism or a group of organisms to their environment. In a broader sense, ecology is the study of the relation of organisms or groups to their environments” (p. 4). Castells (2010) uses a very similar formulation for his definition of social action: “social action at the most fundamental level can be understood as the changing pattern of relationships between nature and culture” (p. 508). As ecology studies relationship patterns among groups and environments, social action studies relationship patterns among culture and nature. This similarity, like the others, suggests (along with the book’s extensive bibliography) that Castells has incorporated ideas from many different sources in articulating this theory of network society.

Once I made those connections, I suggested that Castells offers theoretical, but not an operationalized, theory of the network society; his study of society is used to produce his theory, but he pointedly avoids using the theory to operationalize or predict anything about the network society.

Finally, I decided that the IT revolution is the “event” that triggered the emergence of the network society; without the IT revolution, there is no network society. All of the aspects of the network society, depicted in the table of contents — the global informational economy, the network enterprise, the transformed labor force, real virtuality, the space of flows, and timeless time — all rely on the advances brought about by the IT revolution for their existence.

I found Castells delightfully cogent and engaging. This is surely because of my engagement in a profession that relies on the IT revolution for its existence, but I also found intriguing connections to our emerging understanding of networks as they relate to English studies and to my own nascent ideas about the role of boundaries in network formation and nodal connectivity.

References

Ahlefeldt-Laurvig, F. (2009, December 17). The global society [Creative Commons licensed illustration]. Retrieved from https://flic.kr/p/7oxX6G

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

Foucault, M. (2010). The archaeology of knowledge and the discourse on language. (A. M. Sheridan Smith, Trans.). New York, NY: Vintage Books. (Original work published in 1972)

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

Spellman, F. R. (2007). Introduction. In Ecology for Nonecologists [pp. 3-23]. Lanham, MD: Government Institutes

Reading Notes: The Neuronal Network

My Digital Brain?

The network made of neurons is digital. That’s what I took from this statement about neuron firing: “A neuron can only fire or not fire; there is no ‘slightly activated’ signal from a neuron” (“Neurobiology” 2013, p. 6). I took this activity to be analogous to digital 0s and 1s: a digitally transmitted signal is either on or off (which explains why digital televisions have either perfect pictures or terrible pictures — there are no in-between states of digitally-transmitted data).

Okay, so the brain is not exactly digital. The neuronal network is a composite of electro-chemical impulses within highly specialized organic cellular structures. But I’m intrigued by the idea that neurons either fire or don’t fire. This suggests that brain functions use on/off switches like digital networks; by extension, this suggests that my ability or inability to remember something is not strictly a neuronal function. Other aspects of brain function beyond synaptic firing are at work when it comes to memory retrieval. That’s not exactly relevant to this summary, but it’s an interesting perspective on brain function as it relates to neuron firing.

The Network

The neuronal network is remarkable. I’ll break down the network into its composite parts as I understand them.

Nodes: When I took notes on the reading, I identified cells in the brain as nodes — neurons, glial cells, and other matter (p. 2). But as I reflect on the network, I’ve decided that the nodes in the neuronal network are more likely sensory inputs and physical or chemical outputs: brain function “can be broken down into three basic functions: (1) take in sensory information, (2) process information between neurons, and (3) make outputs” (p. 1). Neurons throughout the body, concentrated in the brain, represent potential pathways for connective activity (processing information) among network nodes. Signals from sensory inputs travel through these pathways into and out of the brain and back to outputs like organs and muscles, which react to sensory inputs.

Connections: This is where neurons excel. Neurons transmit signals from node to node in the network. Transmission is remarkably complex, involving electrical and chemical impulses along with ionic activity along chemical pathways, assisted by myelin, other glial cells, and neurotransmitters along the way. The signals carried along these neuron pathways are physical, chemical, and other responses to sensory inputs.

Meaning: Within the neuron itself, “meaning” is ionic. Neurons send and receive nerve impulses, or “action potential” (p. 4), within their cell structures, then fire such impulses across synapses using neurotransmitters and neural receptors (p. 5-6) to communicate “messages” to and from sensory, mechanical, and chemical nodes. Those messages are encoded from the nodes as chemical or electrical impulses, but within the neuron itself, those impulses travel as ions. Between the neurons, in their own network of axons and dendrites, impulses travel as neurotransmitters and engage many neurons in the activity of transmitting meaning among input and output nodes: “the activation of a single sensory neuron could quickly lead to the activation of inhibition of thousands of neurons” (p. 6).

Visualization of action potential moving through a neuron's axon.

Action potential movement through an axon. Image archive in Unit 10: Neurobiology chapter, part of the Rediscovering Biology online textbook.

Framework and Activity: Neurons represent the framework in which the network is activated. Without sensory input or some kind of output (chemical, physical, emotional), the network is inactive. The framework exists to enable activity, and that framework actually grows or repairs itself over time by generating new neurons from neuronal stem cells (p. 13). Activity in the network is defined as meaning being sent and received through the neuronal connections by input and output nodes. Activity within the neuron itself is defined by chemical processes. As a result, sensory deprivation could halt the network — but so could a lack of neurotransmitter or destruction of myelin or other glial cells. In other words, the network can cease functioning as a result of deactivating nodes or deactivating connectors.

Memory and Learning

Another node I need to consider, aside from sensory inputs and mechanical or chemical outputs, is the hippocampus, “a structure through which all information must pass, before it can be memorized” (p. 12). Based on this description from the text, I’d suggest that, in network terms, the hippocampus might be considered a node with many incoming connections (a la Latour, 2005) or a network gateway that routes (perhaps mediating along the way) neuronal impulses toward memory storage areas. Memory and learning “involve molecular changes in the brain” (p. 10) and represent a chemical output (and input) node in the neuronal network. Sensory input translated into ionic and neurotransmitter meaning travels through the neuronal network through the hippocampus node to be inscribed as molecular change in the brain; this molecular change (memory) becomes a node that can be activated by other sensory inputs to elicit the output response, “I remember!”

Screen Shot 2014-03-31 at 1.29.38 PM

Training the brain. Not sure I buy the Luminosity premise, but if effective, Luminosity probably results in opening new pathways through the hippocampus toward molecular change (memory). Screen capture from Luminosity landing page (tracked via Google Analytics through Google Adwords as a result of selecting a Google search ad).

Application

I was interviewed by a nurse this morning to help determine my eligibility for an insurance product. Much of the interview appeared to relate to my ability to remember things: 4-, 5-, and 6-digit strings of random numbers, series of words, and random words that I incorporated into sentences. In basic terms, I’m pretty sure my neuronal network never transmitted any of these impulses through my hippocampus. That is, I never committed these items to any kind of memory. The impulses (aural, via telephone) were translated into meaning, which I then output vocally (also via telephone), but they did not result in molecular changes in my brain. The tests, I believe, were not intended to test long-term memory, but to ascertain whether I could process data from sensory input through neural transmission to vocal outputs. In short, is my neuronal network functioning as expected? The answer to that question, coupled with my age, profession, eating and exercise habits, and level of alertness will be used in underwriting the policy to determine whether I will be an acceptable risk for the insurance product. I, of course, believe myself quite eligible — but then again, I didn’t remember those ten random words well.

References

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

Neurobiology: Unit 10. (2013). Rediscovering biology: Molecular to global perspectives [Online textbook PDF]. Retrieved from http://www.learner.org/courses/biology/pdf/10_neuro.pdf

[Header image: Neural Network Visualization (made from Babybel cheese bag). CC licensed image from Flickr user Rick Bolin]

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]

Revisiting the Proposal: March 30

Donna Haraway has been credited as one of the first to use the term “cyborg” to describe our relationship with the Digital, as we become “hybrids of machine and organism” (151). The field of English Studies, and in particular Composition … Continue reading

Mindmap #1: The Rabbit Hole

At this point in the class, more questions than answers face me. In one sense I recognize the relative simplicity of a network: a connection of nodes. On the other hand, I quickly complicate my simple definition with questions: Are nodes relatively static? Are they predefined via framework or developed on the fly through the action of the network itself? Do the connections “define” the network, or do the nodes? Or is it the friction between the stasis of the nodes and the activity of the connections that makes the network “work”?

As I considered my object of study, Google Analytics, I also considered the object that Google Analytics studies, namely websites. I’ve created multiple websites in my career, both personal and professional. When I start a website, I start with the basic content that needs to be produced/communicated, then develop an organizational framework into which those content areas can and should appear. We call that framework the IA, or information architecture, and I enjoy creating the IA, either from a never-before-organized collection of content or from previously-created content that needs to be reorganized. My strength as a web manager comes from visualizing and developing the organizational and hierarchical framework for the content. Folder and subfolder structure, relationship of subfolders to folders, pages to folders, and folders to site are among the creative activities of my professional position. In short, I develop the relationship of the nodes to one another and create the connections that visitors will make between and among the nodes, both up and down the IA and page to page in individual folders.

What I realized as I considered Google Analytics is that each “level” of a web site – each folder, subfolder, and subsubfolder (and we try to have only three levels in even the largest sites) is itself a network with connections up and down the IA. A domain is a network. Subdomains within each domain are each networks. Folders within each subdomain are each networks. But they are also nodes. At the domain level, the subdomain is a node on the domain network. At the subdomain level, the folder is a node on the subdomain network. At the folder level, the subfolder is a node on the folder network. And so on down the rabbit hole.

Networks are iterative. My mind map addresses the iterative character of networks as it also starts asking questions that I’d like to answer through the semester. I made the connection between the questions because they are the common thread running through my mind right now. I don’t know enough to start answering yet, but I’m developing ideas and theories.

[Creative Commons licensed image by flickr user RubyGoes]

Object of Study: Google Analytics

I have chosen Google Analytics as my object of study for ENGL 894 Theories of Networks. More specifically, I have chosen the Google Analytics account I manage on behalf of the University of Richmond School of Professional and Continuing Studies. Although this account is a sub-account on the larger University of Richmond Google Analytics roll-up account, I will limit my study to my School’s subdomain’s account.

Google Analytics is a free web activity data collector, aggregator, and reporter. It’s among the most popular web metrics products because it is free, but it is certainly not the only one—other products exist that do a more thorough job of collecting all traffic across multiple web platforms, including advertising and direct email from multiple providers. Google Analytics provides data on all web traffic, but it segments that traffic in reports largely to the benefit of its related products, like Google Adwords and the Google Display Network.

Google Analytics offers web administrators and marketers a window into the activity of users on their website(s). It collects and aggregates data about all web activity on a given domain—in my case, the subdomain SPCS on the richmond.edu domain (spcs.richmond.edu). Any time a person visits any page in the subdomain, Google Analytics collects aggregate data on the visit, including but not limited to user operating system, browser type and version, platform (including desktop, mobile, and tablet), referral source, internal previous and next pages, time spent on the page, time spent within the domain, exit pages, and much more.

Google collects two types of data: metrics (“quantitative measurements of users, sessions and actions”) and dimensions (“characteristics of users, their sessions and actions”) (Google Analytics Academy, 2013). By combining specific metrics and dimensions in a report, web administrators and marketers can answer specific questions about visitor behavior, like which web pages generate more traffic than others and which pages result in visitors remaining on, rather than exiting, the website. As a result, Google Analytics provides key quantitative data to support specific goals, including increased time on site and increased traffic to a specific page. Adding e-commerce and online advertising data collection in Google Analytics offers a complete picture of the effectiveness of online communication efforts across multiple digital channels and platforms.

Google Analytics offers several interesting uses in English studies. Since web sites, especially in higher education, are written as communication, English studies should be able to use Google Analytics to measure whether written communications are effective. Professional communication pedagogy should address specific, measurable ways to determine whether communication is successful; by tracking aggregate visitor behavior on a specific communication goal, like a call to action, writers can hone messages to communicate more effectively.

Google Analytics is a tool for analysis. It collects metadata about web visits as a means to understanding the way visitors navigate a set of web pages. Its analytical tools and methodology are ripe for analysis and critique. Google obfuscates its search algorithm; Google Analytics offers a window into the results of searches, which helps administrators and analysts reverse engineer Google’s search algorithm. As search becomes the default way people engage with the web, Google’s social and economic clout offer intriguing opportunities to open and close markets, to serve the underserved or to underserve a specific population. Obfuscating the source of this power invites social critique, a favored method for English studies.

Google Analytics is a window into the remarkably detailed visitor data Google collects on each visit to a given web site. Such aggregate data provides Google a powerful tool to offer online advertisers that seek to target specific demographics. Scholars in English studies have opportunities to consider the potential social impact of collecting and sharing such data—to analyze and critique collection methodologies, social implications, marketing efforts, and communication channels. The data are quantitative; English studies provides an opportunity to examine, qualify, and put a human face on the data results.

If we consider a single web domain (or, in this case, subdomain) a node in a global virtual network, Google Analytics is the shadow “metanetwork” informing the node. Whether we consider the node the subdomain itself, a folder in a subdomain, or an individual page within a folder, Google Analytics is the shadow network providing metadata at the intersection of the human and the electronic, of the virtual node and the visitor node. Its ability to function as a network while informing about other networks is exciting and complex, ideal fodder for theorizing networks.

Reference

Google Analytics Academy. (2013, October). Key metrics and dimensions defined [Video transcript]. Digital Analytics Fundamentals. Retrieved from https://analyticsacademy.withgoogle.com/assets/pdf/DigitalAnalyticsFundamentals-Lesson3.2KeymetricsanddimensionsdefinedText.pdf

[Behavior flow visualization courtesy University of Richmond SPCS Google Analytics account]