Sunday, December 16, 2018

#el30: A Communal Experience

This is my community experience for #el30, in which Stephen asks us to "create an assignment the completion of which denotes being a member of the community."

I am still focused on text, so I took one post that mentioned community in either the title or the text from the following #el30 blogs:
I then entered each link into a new analysis space at Voyant-tools to create a collection of #el30 posts about community. For the sake of this particular analysis, I removed the names of months from the word cloud as they were clouding (pun intended) the results. Voyant generated the following word cloud:

The word cloud presents the most common nouns and verbs from all of the posts; however, the word cloud is live, which means you can change it. Click on the Scale drop down in the lower left corner to select a specific post, and slide the Terms slider to include more or fewer words. Your assignment is to play with the various posts and collections of terms to create different word clouds and to see if any meaning emerges for you. Then leave a comment on this post to tell the rest of us what you learned.

My failure to post most weeks during the MOOC does not reflect my interest; rather, I'm at the end of my school term, in the middle of some vexing family issues, and about to leave for two weeks in the Bahamas (so don't feel sorry for me). I just couldn't focus on writing, but I did much of the reading and watched most of the videos. I'll carry this conversation forward for the next half year, I suspect.

Thanks to Stephen and all for doing this.

Saturday, November 17, 2018

#el30: Prepositions on the Edge of Identity

Last week, Stephen Downes assigned an identity graph for those participating in #el30. Like Jenny Mackness and Mathias Melcher, I was initially perplexed that the graph “should not contain a self-referential node titled ‘me’ or ‘self’ or anything similar”. Surely, I thought, any picture of my connections should include me, right? Then I had a light-bulb moment and realized that the web of connections, the graph, was me, and that this view of identity is in keeping with Downes' connectivism theory which says, among other things, that meaning emerges within the network of relationships (edges) among nodes rather than in a single node itself. I subscribe to this belief, but old mental habits are difficult to break. I still want to see me as … well, just me, a single, individual node. So building an identity graph could be a therapeutic exercise for me.

I examined the graphs built by others in #el30 for some clues about how to go about this—I always like a model to use even if I intend to violate the model. Melcher based his identity graph on his Twitter and library interests. Mackness used a variety of life events, roles, and locations. Roland Legrand based his map on his spiritual/philosophical beliefs and life roles. I found them all to be wonderful insights into the people who created them, but none of them clicked for me—not wrong, mind you, just no click.

For one thing, I was troubled by the edges, the links, between the nodes. The nodes at least have labels, but the links are nothing more than a thin line from one node to another. This strikes me as a serious oversight. If the meaning is in the relationships, then the links ought to mean something. In most of the graphs I've examined and the tools I've tried for generating graphs, the links are just skinny little lines. At best, they might have an arrow to indicate directional flow. That did not satisfy me.

Then, I teach writing, and I write. Writing seems to be a solid chunk of reality out of which to build an identity graph, and the chunk is definitely related to how I identify myself. Moreover, writing includes those built-in links (prepositions, conjunctions, commas, and other linking devices) that can add texture and color to the edges. Of course, most language scholars (both poetic and rhetoric) tend to favor the nodes—the nouns and verbs, or actors and actions—of writing and ignore the little words. We don't capitalize them in titles, for instance (Gone with the Wind); yet, it's the little words that connect the big words to each other and create much of the meaning, as I discussed in a handful of posts as part of Rhizo14 four years ago. Prepositions were on my mind because of some remarks by Michel Serres. In his book Conversations on Science, Culture, and Time (1995) with Bruno Latour, Serres suggests that prepositions mean almost nothing or almost anything, which turns out to be about the same thing, but that they do the critical work of arranging and connecting the actors, actions, and settings. It seemed rich at the time, but I did not pursue the ideas very far.

In his earlier essay "Platonic Dialogue" in Hermes: Literature, Science, Philosophy (1982), Serres says, "writing is first and foremost a drawing, an ideogram, or a conventional graph" (65). I do not think that Serres is speaking of graphs as we have this week in #el30—he almost certainly means something like a mark or picture—but I want to play with this connection between writing and graphs. My intent is to build an identity graph using the #el30 posts that I have written thus far. The four posts result in a fairly short 3,147 word document when the text is aggregated. I'm using Voyant tools to analyze the #el30 text, which I also used back in Rhizo14, and you can see my Voyant dashboard here. I also used a dashboard that distinguished each post here. This dashboard has some interesting data about my posts as posts within my blog, but I will not use this dashboard in this post.

Unfortunately, Voyant by default uses a stopword list to eliminate prepositions, conjunctions, and other classes of small words from its tools, deeming those words as irrelevant and mostly meaningless. The documentation for Voyant says it this way: "Typically stopword lists contain so-called function words that don’t carry as much meaning, such as determiners and prepositions (in, to, from, etc.)." I'm dismayed but not surprised. Prepositions get no respect from writers, rhetors, and grammarians. However, I intend to use prepositions as edges in my identity graph. I suspect that the prepositions and other connectors will give the links texture, color, spin, and direction that will enrich the meaning of the local connection and the network of connections.

You can see a word cloud of my posts here:

We are all familiar with word clouds, but I'm thinking now that they are proto-graphs with all the nodes and none of the edges. Thus, they are limited in what they reveal. I do, however, like the different sizes and colors of the nodes, and I think I want a graph tool that keeps the different sizes and colors of the nodes and includes the different edges. Voyant suite of tools does not quite do that—or at least, I have not found the tool that does.

So I'm following Jenny Mackness' lead and also using Matthias Melcher’s think tool – Thought Condensr. I thought I would map the top 5 words in my posts, but I managed to do just one: data, the most common major word in my four posts. The graph looks like this:

My writing over the past month reveals a preoccupation with data (the most common of the big words in my posts at 39 occurrences), and the identity graph above expresses my particular orchestration of nodes and edges that identifies me like a thumbprint. Everyone in #el30 is interested in and thinks about data, but I daresay that none have a print like mine. Yes, they have similar prints, perhaps, but not exactly this one. Just as all fingerprints have lines, arches, loops, and whorls, none have them arranged in the same way. That graph above identifies Keith Hamon—or at least a bit of him at a certain scale. This graph orchestrates drowning in data from backyard (in general, read the node/edge clusters from the blue node on the left, through a green connector, then to the red data node, and on to another green connector and a yellow node) with the other clusters of nodes and links to create a unique yet still recognizable fractal image.

Unfortunately, I cannot identify a given cluster of nodes and edges, so you can actually create new ones by following left into data and then out to any other node. You can also read from right to left to create even more clusters that generate different meanings. I think these are limitations of the graphing tools, my skills with the tools, or both. I need a graphing tool that will allow me to identify both nodes and edges and the resulting clusters and to view them in a 3-D or 4-D space. 2-D is too limiting.

I realize, however, that I've made the same error that the Voyant Tools creators did: I've put the noun (in this case, data) in the center, putting all the focus on it and building all the meaning around it. I should have put the focus on one of the prepositions—say, of with its 104 occurrences. I simply do not have time just now to graph all 104 instances of of, so I did just the first 10, and it looks like this:

Look at what a workhorse this little word of is. Consider how it connects all these nodes to create meaning at various scales, to make this particular arrangement of nodes and edges identifiably me. Consider one cluster: University of Miami. All of us in #el30 have some university node, but I may be the only one with a UM node. Even if another of us has a UM node, all the other nodes stitched together by of quickly identify me. I'm the one with a University of Miami node and a movement of energy, matter, information, and organization node. Add the 102 other of clusters, and you've pinned me to the wall. That's me.

I'm not really satisfied with these graphs, but I think they are a wonderful start to thinking about writing and how it creates an identity. I'm very happy Downes assigned this. I'm even happier that I tried it. Seems it was great therapy and substantial learning for me.

Sunday, November 4, 2018

#el30: Interpreting the Cloud

The point of the computing cloud for me has been the continued abstraction of data and services from the computing platform. I've been using computers since early 1980s (In 1982, I wrote my dissertation on the University of Miami's UNIVAC 1100), and I became a Mac user in 1987, so I am well-versed in the problems with exchanging data on one platform with users on another platform. I'm glad those wars are mostly over. I now use a PC at work, a MacBook Pro at home, an Asus Chromebook on the road, and an iPhone everywhere. The underlying hardware and operating systems are almost transparent windows to my online data, documents, and communications. I'm writing this post at home on my MacBook, but I've written posts on all my devices, including my iPhone. I also no longer ask my students what kind of device they have when I make an assignment as they all have at least a smartphone (again, I don't care which) that will let them access the class wiki and do the work. However, they do need a Google account to do most of the work.

And here is the one more platform layer that I want to remove: Google (or Facebook or Twitter). Some of the technologies that Tony Hirst and Stephen Downes discussed in their video chat (over Google, of course) seem to be taking the first steps toward separating a cloud service (say, video chat) from a monolithic platform such as Google. This continues a long progression in computing: we were freed from particular hardware, then from particular operating systems, and maybe soon from particular cloud platforms. So someday I may be able to fire up a container (made by Tony Hirst and released into the commons) on any of my half-dozen devices and hold a video chat with others peer-to-peer on their different devices and containers. I may even write my own containers for special services and release those containers into the commons where they can be used or remixed into different containers to render different services.


My understanding of complex systems is all about the movement of energy, matter, information, and organization within and among systems. As a complex system myself, I self-organize and endure only to the degree that I can sustain the flows of energy (think food) and information (think EL 3.0) through me. The cloud is primarily about flows of information, and the assumption I hear in Stephen's discussion is that I, an individual, should be able to control that flow of information rather than some other person or group (say, Facebook) and that I should be able to control the flow of information both into and out of me. I find this idea of self-control, or self-organization, problematic—mostly because it is not absolute. As far as I know, only black holes absolutely command their own spaces, taking in whatever energy and information they like and giving out nothing (well, almost nothing—seems that even black holes may not be absolute).

It helps me to walk outside for discussions such as this, so come with me into my backyard for a moment. The day is cool and sunny, so I'm soaking in lots of energy from sunlight. I've had a great breakfast, so more energy. I've read all the posts about the cloud in the #el30 feed, so I have lots of information. Of course, I'm pulling in petabytes of data from my backyard, though I'm conscious of only a small bit. Even with the bright light, I can see only a sliver of the available bandwidth. I hear only a little of what is here, and I certainly don't hear the cosmic background radiation, the echo of the big bang that is still resonating throughout the universe. I'm awash in energy and information. I always have been. Furthermore, I can absorb and process only a bit (pun intended) of the data and energy streams flowing around me, and very little of this absorption is my choice. Yes, if the Sun is too bright, I can go back inside, put on more clothing, or put on sunscreen, but really, what have I to do about the flow of energy from the Sun? And what have I to do with the house to go into, the clothing to put on, or the sunscreen. All of those things are complex systems that came to me through other complex systems (bank loans, retail stores, manufacturing factories, Amazon, and my own income streams). Most of the energy and information streams that I tap into owe little to me, not even the energy and information that I feedback.

In his post "Post-it found! the low-tech side of eLearning 3.0 ;-)", AK quotes George Siemens as saying something like "what information abundance consumes is attention", and this gets me, I hope, to a point about all this: Siemens is talking about only a tiny subset of information available to me, even though it tends to be the information that consumes most of my attention. There are other far more important streams of energy and information that I should attend to, I think.

Ahh ... maybe this is my point: even if I can avail myself of more access to more information, I'm already drowning in data. What I desperately need are better filters for selecting among the data and better models for organizing that selected data into useful, actionable knowledge. This is what my students need. Everyone in the U.S. needs better filters and models, especially with national elections on the horizon. In this sense, we are not so different from all the humans and other living creatures who have existed, except that our social systems are so much more complex and complicated than those that came before. What data do I trust, and after I've determined that, how do I arrange this data into actionable knowledge? Facebook and Google are filtering data for me now, and they are even arranging that data into actionable knowledge, but I don't think I trust them. Can the cloud help me interpret the cloud?

Saturday, October 27, 2018

#el30 Data and Models

I should be grading student documents this morning, but I'm thinking about #el30. I may have an assessment of that next week.

Anyway, as I was reading some posts about Data, I was struggling with our previous discussion about the differences between human and machine learning, when something that AK wrote sparked some coherent ideas (at least dimly coherent for my part). AK said: "This got me thinking about the onus (read: hassle) of tracking down your learning experiences as a learner. ... As a learner I don't really care about tracking my own learning experiences."

I thought, no, I, too, don't want to track all my learning experiences. Tracking all those experiences would take all my time, leaving no time for more learning, much less time for grading my students' papers. So maybe computers can be useful for tracking my learning experiences for me? A computer can attend me--say, strapped to my wrist, in my pocket, or embedded in my brain--and collect data about whatever my learning experiences are. After all, computers can collect, aggregate, and process data much faster than I can, and as Jenny notes, computers don't get tired.

But what data does a computer identify and collect? Even the fastest computer cannot collect all the bits of data involved in even the simplest learning task. How will the computer know when I'm learning this and not that? Well, the computer will collect the data that some human told it to collect. Can the computer choose to collect different data if the situation changes, as it certainly will? Perhaps. But again, it can only ever collect a subset of data. How will it know which is the relevant, useful subset? The computer's subset of data may be quantitatively larger than my subset, but will it be qualitatively better? How might I answer that question?

Turning experience into data is a big issue, and I want to know how the xAPI manages it. Making data of experience requires a model of experience, and a model always leaves out most of the experience. The hope, of course, is that the model captures enough of the experience to be useful, but then that utility is always tempered by the larger situation within which the learning and tracking take place. Can a computer generate a better model than I can? Not yet, I don't think.

If both the computer and I are peering into an infinity of experience, and I can capture only about six feet in data while the computer can capture sixty feet, or even six hundred feet, we are both still damned near blind quantitatively speaking. Reality goes a long way out, and there is still something about constructing models to capture that reality that humans have to do.

I've no doubt that computers will help us see farther and wider than we do now, just as telescopes and microscopes helped us. I've also no doubt that computers will help us analyze and find patterns in that additional data, but I'm not yet convinced that computers will create better models of reality without us. When I see two computers arriving at different views of Donald Trump and arguing about their respective views, then I might change my mind.

The #MeToo Text: From Documents to Distributed Data #el30

This week's Electronic Learning 3.0 task is about distributed data, and it gives me a way to think about the #MeToo document that has occupied me for the past year and that has been the topic of several posts in this blog. In short, I take the #MeToo text (all several million tweets of it and more) to represent a new kind of distributed document that is emerging on the Net. Thus, it may be a manifestation of the kind of shift in how we handle data that Downes discusses.

Downes introduces his topic this way:
This week the course addresses two conceptual challenges: first, the shift in our understanding of content from documents to data; and second, the shift in our understanding of data from centralized to decentralized. 
The first shift allows us to think of content - and hence, our knowledge - as dynamic, as being updated and adapted in the light of changes and events. The second allows us to think of data - and hence, of our record of that knowledge - as distributed, as being copied and shared and circulated as and when needed around the world.
I teach writing--both the writing of one's own and the writings of others--which since the advent of Western rhetoric in Greece some three thousand years ago has focused on centralized documents. By that I mean that the function of a document (this blog post, for instance, or a poem or report) was to gather data, organize that data into a format appropriate for a given rhetorical situation, and then present that data in a single spoken or written text. This is generally what I teach my students to do in first-year college composition. This is what I'm trying to do now in this blog post. This is, at least in part, what Downes has done in his Electronic Learning 3.0 web site. Most Western communications has been built on the ground of individual documents or a corpus of documents (think The Bible, for instance, or the Mishnah or the poems of John Berryman).

This idea of a centralized document carries several assumptions that are being challenged by the emergence of distributed data, I think. First, the Western document assumes a unified author--either a single person or a coherent group of people. Western rhetoric has a strong tendency to enforce unity even where it does not exist (think of the effort to subsume the different writers of The Bible, for instance, under the single author God). The Western notion of author-ity still follows from this notion of a single, unified author, and the value and success of the document depends in great part upon the perceived authority of this author.

Along with a single, unified author, the Western document assumes a unity within itself. A document is supposed to be self-contained, self-sufficient. It is supposed to include within it all the data that is necessary for a reader to understand its theme or thesis. I don't believe that any document has ever been self-sufficient, but this is the ideal. A text should be coherent with a controlling theme (poetic) or thesis (rhetoric). The integrity and value of the text is measured by how well the content relates to and supports the theme or thesis.

And of course, a document should have a unity of content. It should have a single narrative, a single experience, a single argument. Fractured, fragmented narratives bother us, and they never make the best-seller lists. Incoherent arguments seldom get an A or get published.

There may be other unities that I could mention, but this is sufficient to make my point that we have a long history of aggregating, storing, and moving data in documents with their implied unities. And then along comes #MeToo: a million tweets and counting over days, weeks, and months. We have this sense that surely #MeToo is hanging together somehow, but is it really a single text?

Well, not in the traditional sense. It has no unified author. Just when we thought that Alyssa Milano started it, we learn that some other woman, Tarana Burke, used the phrase ten years ago. #MeToo isn't even a unified group. A million women are not a unified group. It has no unified thesis. It isn't even an argument. There is no dialectic or rationale. It has no unified content. We think it does because of the single hash tag, but each woman brings a unique set of experiences to her tweet: some have a leer or catcall, some gropings, others rapes or years of beatings. All of them have something different, something unique. They cover the gamut, the field, the space.

#MeToo is a swarm, and we really don't like swarms. Who's speaking here, to whom, and about what? What's the point? And what kind of document is this? How do I read it? How do I respond?

#MeToo is a rhizome, a fractal, and I'm thinking we will come to write and to read this way. We will think this way. Perhaps we always have, and our documents obscured that for us. #MeToo makes explicit a million neurons firing.

And finally, I must recognize that #MeToo could neither have been written nor read without our technology. This way of knowing, thinking, and expressing is possible only with help--in this case, Twitter to write it and somewhat read it--though reading millions of tweets is rather impossible for a single human to do. We need the data analysis powers of our computers to even approach a comprehensive reading of #MeToo. We need something like Valentina D'Efilippo's reading strategies and tools in her article "The anatomy of a hashtag — a visual analysis of the MeToo Movement".

I'm wondering, then, what happens when not only data is distributed and decentralized, but when documents themselves become distributed and decentralized. Is this fake news?

Monday, October 22, 2018

Being Human among Computers: #el30

With a number of other online colleagues, I'm starting a new MOOC with Stephen Downes entitled "E-Learning 3.0". According to Stephen's introduction:
This course introduces the third generation of the web, sometimes called web3, and the impact on e-learning that follows. In this third generation we see greater use of cloud and distributed web technologies as well as open linked data and personal cryptography.
The first week featured a Google Hangout between Stephen in Canada and George Siemens in Australia. I've posted the video here, starting it about seven-and-a-half minutes in to avoid the setup issues.

As Jenny Mackness notes in her blog post about the conversation, Siemens and Downes wax philosophical in their conversation, centering "around what it means to be human and what is human intelligence in a world where machines can learn just as we do."

While I understand the fascination of such a question as computer technologies increasingly approximate many of our intellectual capabilities, in some ways the question seems moot. For me, part of what it means to be human is to use tools and technologies that enhance our innate human capabilities. Admittedly, most of our early tools enhanced our physical capabilities, making us stronger and faster and warmer, but from the beginning, we created technologies that enhanced our intellectual capabilities. I think of language as a technology, and I am not yet convinced that computers will change us more than language in both spoken and written forms has already done. I can almost see computers as a refinement and extension of language, which started with speech, eventually developed into writing—making marks also led to math and drawings—and is being expressed now through computers. Few things distinguish us from other life forms as much as our tools and technologies do.

Did Shakespeare write Hamlet or did the English language? Well, both actually.

Part of the fascination of this question about human vs. computer intelligence comes from our apprehension that computers will become more powerful than we are. This is an old fear, as the American folk tale of John Henry demonstrates, but for me, the lesson of John Henry is that we will continue to use computers to make us smarter despite our fears. I suppose the fearful prospect is if computers will use us to make themselves smarter or if they will simply come to ignore us, having become so smart themselves that our abilities add nothing to them. I don't think they will destroy us; rather, they'll abandon us. This is a problem mostly if you think that humans are the smartest thing in the universe and that computers will usurp our position. It seems rather chauvinistic to think that humans are the crowning achievement in this wondrously large and varied universe. The odds are surely against it, I think.

Almost all complex systems that I know about can learn: taking in information from the ecosystem, processing that information, making structural adjustments to better fit to their environments, and then feeding back information into the ecosystem, which likewise is trying to make a better fit for itself. I have no doubt that computers will do the same, and if our ecosystem comes to include smart machines, then we and the rest of the ecosystem will have to adapt to those new entities. The universe will manage that adaptation quite nicely and count itself more advanced for it.

But that's the long game. In the short game, I am keen to explore how smart machines can help me and my students learn differently, maybe better.

Saturday, July 14, 2018

RhizoRhetoric: ANT Roots

I'm reading Bruno Latour's 2005 book Reassembling the Social: An Introduction to Actor-Network Theory, and the implications for a rhizomatic rhetoric are worth careful exploration over several posts. This is the first.

The first chapter of Latour's book presents his reasons for devising actor-network theory (ANT) and for writing the book: his discomfort with the assumption by conventional sociology of the social as an existing domain within which to embed and define groups. Latour prefers to start with the emerging group to follow the connections and interactions both within the group and with its environment to uncover how the social emerges. To my mind, Latour wants to define from the inside out rather than from the outside in. Following the actual, existing traces of the group's activities means being willing to follow tracks that might not be recognized as social from the perspective of any given social theory.

In her review of the book, Barbara Czarniawska begins with a quote from Giles Deleuze: "There is no more a method for learning than there is a method for finding treasures...(Giles Deleuze, Difference and Repetition, 1968/1997: 165)". I like this nod to Deleuze as recognition of the more open-ended approach both Deleuze and Latour bring to their studies. Learning demands a willingness to re-examine existing structures, points of view, methods, and theories and then to reinforce those that prove helpful and to change or abandon those that prove harmful. Our existing knowledge both enables us to know more and limits what more we can know. When we already know what will happen, then we are more likely to miss what actually happens. Deleuze and Latour are both looking for ways around this dilemma of knowledge. Shunryu Suzuki says it best for me in his book Zen Mind, Beginner's Mind (1973): "In the beginner’s mind there are many possibilities, but in the expert’s there are few." Suzuki, Deleuze, and Latour are, of course, speaking of issues in the complex domain rather than the simple or complicated domains, as defined in Dave Snowden's Cynefin framework. Like these thinkers, I think that most of life is complex, and I'm certain that rhetoric is, despite the myriad attempts by rhetoricians to render it simple or at least merely complicated.

In a sense, then, all of these fellows, and certainly Latour, are resisting the tendency to view life through too narrow a lens, to put our experiments into too small a box, to render simple or no more than complicated that which is rightly complex. Latour makes this clear when he compares the shift in thinking required by ANT with the shift in thinking required by modern physics. He says:
A more extreme way of relating the two schools is to borrow a somewhat tricky parallel from the history of physics and to say that the sociology of the social remains ‘pre-relativist’, while our sociology has to be fully ‘relativist’. In most ordinary cases, for instance situations that change slowly, the pre-relativist framework is perfectly fine and any fixed frame of reference can register action without too much deformation [Cynefin's simple/complicated domains]. But as soon as things accelerate, innovations proliferate, and entities are multiplied [Cynefin's complex/chaotic domains], one then has an absolutist framework generating data that becomes hopelessly messed up. This is when a relativistic solution has to be devised in order to remain able to move between frames of reference and to regain some sort of commensurability between traces coming from frames traveling at very different speeds and acceleration. Since relativity theory is a well-known example of a major shift in our mental apparatus triggered by very basic questions, it can be used as a nice parallel for the ways in which the sociology of associations reverses and generalizes the sociology of the social. (12)
I particularly like this comparison of ANT sociology with modern physics, as it seems to me that modern physics has moved us from the modern world of the Enlightenment and Newton into the postmodern world of Einstein, Bohr, Deleuze, and Carlos Casteneda. I mention Casteneda because he provides the perfect image of ANT years before Latour thought of it. Also, Deleuze and Gauttari mention Casteneda in their book A Thousand Plateaus, where they note that in The Teachings of Don Juan, the Yaqui sorcerer Don Juan Matus gives his student Carlos instructions about how to cultivate a garden of hallucinogenic herbs:
Go first to your old plant and watch carefully the watercourse made by the rain. By now the rain must have carried the seeds far away. Watch the crevices made by the runoff, and from them determine the direction of the flow. Then find the plant that is growing at the farthest point from your plant. All the devil's weed plants that are growing in between are yours. Later … you can extend the size of your territory by following the watercourse from each point along the way. (ATP, 11)
This makes Latour's point quite nicely and most graphically: start with an initial observation of a functioning group, then follow the traces (the watercourses and crevices) that are actually there (not the ones you think should be there based on your fixed, rectangular theory of what a garden should look like), scribbling like mad to capture as much as you can.

Though as often happens, the poets and prophets were there first. In a 1956 interview in The Paris Review, William Faulkner says of theory: "Let the writer take up surgery or bricklaying if he is interested in technique. There is no mechanical way to get the writing done, no shortcut. The young writer would be a fool to follow a theory. Teach yourself by your own mistakes; people learn only by error." He says of his own method for writing Nobel-quality novels: “It begins with a character, usually, and once he stands up on his feet and begins to move, all I can do is trot along behind him with a paper and pencil trying to keep up long enough to put down what he says and does.”

This may be the heart of ANT: start with an observation and trot along behind to see where it goes, what it connects to, and what energy and information it exchanges. There's your novel, your sociology, or your physics. Or your rhetoric.

Czarniawska explains Latour's intentions for his book this way:
The question for social sciences is not, therefore, ‘How social is this?’, but how things, people, and ideas become connected and assembled in larger units. Actor-network theory (ANT) is a guide to the process of answering this question. (1)
Latour devotes much of his first chapter to distinguishing his approach to sociology from established approaches. As Czarniawska says, "Students of the social need to abandon the recent idea that 'social' is a kind of essential property that can be discovered and measured, and return to the etymology of the word, which meant something connected or assembled" (1). Latour says it this way:
Even though most social scientists would prefer to call ‘social’ a homogeneous thing, it’s perfectly acceptable to designate by the same word a trail of associations between heterogeneous elements. Since in both cases the word retains the same origin—from the Latin root socius— it is possible to remain faithful to the original intuitions of the social sciences by redefining sociology not as the ‘science of the social’, but as the tracing of associations. In this meaning of the adjective, social does not designate a thing among other things, like a black sheep among other white sheep, but a type of connection between things that are not themselves social. (5, italics in original)
A social group for Latour is not defined from the outside by measuring how well the group matches a definition of social, regardless of how sophisticated or admirable the definition might be; rather, a social group is defined from the inside as the researcher crawls into the group to trace the associations at work within the group and between the group and its environment. The working out of these associations -- these dynamic exchanges of energy, information, matter, and organization among actors -- define the group. For Latour, this is the work of the ANT sociologist.

I am deeply attracted to this orientation to study, analysis, and understanding, and it helps explain what one of my writing groups tried to do in a recently published paper "Pioneering Alternative Forms of Collaboration", in which we explored how our online group formed to write several documents and presentations about the #rhizo14/15 MOOCs we all participated in. We wrote this particular paper from the inside out, or tried to, and I think we were able to capture a few points that we might have missed had we done a traditional rhetorical study of our work together. In this document, we did not start with a rhetorical definition of how academic scholars should collaborate online to write their documents; rather, we tried to trace what we actually did to see if we could figure out how and why it worked. I'm proud of this paper, though I think we could do a much better job of it now than we did then. Still, for me it was a step in a rewarding direction. And this is worth adding: it was not a destination, just a direction. We will not likely create a swarm method of scholarly writing for other groups to follow, though we may trace a few paths that others may walk, more or less. That remains to be seen.

So like Latour, I can orient myself to my studies by starting with an observation of an actor/action and then tracing as carefully as possible the connections and interactions within the actor and between the actor and its environment of myriad other actors. I will almost certainly rely on my existing models of reality to try to understand the actor/action, but I also must be willing to relax those models to allow for the connections and interactions not included in my model. Like an ant, I must be willing to follow any trail -- especially those that lead to wrong turns and dead-ends on the map of my theory -- for that is precisely when I am positioned to learn.

Thursday, April 5, 2018

RhizoRhetoric: 7 Self-Organize

The ability to adapt, or self-organize, is Cilliers' final characteristic of complex systems. He says it this way:
Complex systems are adaptive. They can (re)organize their internal structure without the intervention of an external agent.
I reveal my professional bias by saying it this way: complex systems can learn. And though we typically think of learning in human terms, learning is a characteristic of all complex systems from microbes and bacteria to galaxies. Some systems learn incredibly slowly (rocks, for instance, seem to adapt over millions of years) and some quickly, but all take in energy and information from their environments and reorganize themselves to accommodate changes in that environment.

Self-organization is key to a writing swarm, and in some ways, I can consider all of the previous characteristics of complex systems as the foundation to this: the ability to learn and self-organize. A writing swarm is a learning hive, and I think we all learned much.

First, we all learned more about the tools we were using. All of us in the swarm are computer literate and network savvy, yet each of us learned to use a new tool (for me, Slack) and to use new techniques for familiar tools. Here we can easily see the wonderful creative tension between memory, or existing knowledge which strives to keep the practices and structures that it has, and dynamic new knowledge which strives to change the swarm's practices and structures. Learning requires this interplay between the ability to change and resistance to change. Self-organization requires both the ability to change and the ability to resist change, adaptability as well as resilience.

Then, we all learned more about how academics are interacting on the Net, both in planned MOOCs and in looser, unplanned swarms. All of us in the swarm are highly educated academics with a substantial body of knowledge to bring to the swarm, and this body of knowledge forms a rich backdrop and resource within which to test, temper, and integrate new knowledge. As such, it both enables our ability to add to knowledge and brakes any impulse to change too quickly.

Finally, we all learned more about each other. Though few of us have met physically, all of us have gotten to know each other virtually. We are colleagues, and in some cases, friends. Whether friends or not, we trust each other, and in the few cases where trust has been undermined, the offending or offended persons have left the swarm.

In many ways, the self-organization of our particular swarm is mediated by the documents that we write. Unfortunately, the formal character of a finished, printed document obscures the tracks of the interactions that led to that formal arrangement. It's something like a formal family portrait that shows too little of how all these people are connected and interact. The history feature in Google Docs is able to reveal some of the traces of composition, and it is a vastly underutilized feature of Docs that merits substantial research. The data is there and should be mined.

We have much to say about how our swarm learned, but I wonder if we can say it all. I suspect that some learning takes place at the swarm scale, somewhat over our heads. I base this speculation (and it really is speculation) on the analogy of the bridges and rafts that army ants can build to overcome obstacles. In her Quantamagazine article "The Remarkable Self-Organization of Ants", Emily Singer explains how army ants build bridges of themselves to get the foraging swarm across gaps in their path, "a marquee example of a complex decentralized system that arises from the interactions of many individuals," much like our writing swarm. Singer says:
Bridges are built based on simple rules and possess surprising strength and flexibility. As soon as an ant senses a gap in the road, it starts to build a bridge, which can reach a span of tens of centimeters and involve hundreds of ants. Once the structure is formed, the ants will maintain their position as long as they feel traffic overhead, dismantling the bridge as soon as the traffic lightens.
The key point for me is that the ants are mostly responding to local conditions and to the few ants immediately around them. The bridge is an emergent property at a higher scale of the ants' local and simpler behavior. I have to wonder if any of the ants actually knows that it is building a bridge, or is it just doing what makes sense at the moment? Similarly, did our swarm learn things that are literally over our individual heads? Well, this will take much more thought.

RhizoRhetoric: 6 Emergence

The sixth characteristic of complex systems covers the concept of emergence:
The behavior of the system is determined by the nature of the interactions, not by what is contained within the components. Since the interactions are rich, dynamic, fed back, and, above all, nonlinear, the behavior of the system as a whole cannot be predicted from an inspection of its components. The notion of “emergence” is used to describe this aspect. The presence of emergent properties does not provide an argument against causality, only against deterministic forms of prediction.
The identity and value of a writing swarm is not the result of some characteristics or features innate to the various people in that swarm or to the various tools they use or the topics they write about; rather, the identity and value of a writing swarm emerges from interactions and exchanges among the actors and activities of the swarm. For instance, the topic of swarm writing was not a concept that we were all thinking about; rather, the topic emerged from our discussions and our interactions as we struggled to identify what we were observing people do in the #rhizo14/15 MOOC.

The swarm emerged and was defined from within, not from without. For instance, the various actors in the swarm were not selected by MOOC leader Dave Cormier beforehand based on some specific expertise of each and some goal of #rhizo14/15; rather, the swarm organized itself around discussions and tasks that interested various people at various times. People dropped in and dropped out of the swarm for their own reasons and according to their own trajectories, and the characteristics of the swarm changed as the swarm's actors and tasks changed. Enough actors have persisted to maintain the identity and some of the memory of the swarm, but enough actors have changed so that the swarm can respond to new ideas and tasks and take new directions.

As Cilliers points out, this is not a chaotic process that undermines causality and reason. The trajectories that all actors and activities in the swarm follow can be traced back to sufficient causes; however, the complex interactions of the actors do undermine our ability to predict absolutely what the swarm will do next. There is no guarantee that past activities will be accurate predictors of future activities. While the swarm does have memory that preserves past activities and structures, it is also open to new energies and information that can change those activities and structures.

This is all a way of saying that a writing swarm should expect novelty. What emerges from the swarm cannot be absolutely predicted by even a thorough examination and analysis of the constituent elements. The properties of a complex system such as a writing swarm emerge at a different scale, and the swarm develops its own agency, rules, and trajectory. The unexpected is not necessarily a sign of malfunctioning, and a writing swarm should not suppress the unanticipated new out of hand.

Tuesday, April 3, 2018

RhizoRhetoric: 5 Memory

The fifth characteristic of complex systems according to Paul Cilliers reads this way:
Complex systems have memory, not located at a specific place, but distributed throughout the system. Any complex system thus has a history, and the history is of cardinal importance to the behavior of the system.
As a complex system organizes itself, it develops memory: those repeated and eventually stubborn processes and structures that the system depends upon for its identity and functioning. In a sense, these stubborn habits of body and mind are the counterpoints to the dynamism that allows the complex system to learn new things. Memory is vital to complex systems, and eventually serves as an invaluable aid to a complex system, automating some processes and tempering if not dampening new energies and information. As the memory of a complex system develops and becomes stronger, it enters into what Edgar Morin calls a dialogic relationship with a system dynamism, or in educational terms: growth, learning, and development. Memory resists change, and change modifies memory. Complex systems, then, come to rely upon the constant tension between memory and learning, with memory sometimes taking the upper hand, and sometimes losing to new knowledge. This tension is not dialectical as there is no resolution or synthesis; rather, the complex system depends upon the constant, non-equilibrium and tension between memory and change.

Our writing swarm and its documents, for instance, cannot be understood and accounted for without some knowledge of its history and memory. Most immediately, the people in our swarm all shared the #rhizo14/15 MOOCs facilitated by Dave Cormier of the University of PEI. #rhizo14/15 takes its name (I think of it essentially as one course) from the rhizome of Deleuze and Guattari's philosophical work A Thousand Plateaus, and the swarm is in many ways another metaphor like a rhizome for complex systems. Thus, all of us were primed by #rhizo14/15 for thinking in terms of swarms and complex open systems.

Then, we are all connected to higher education either as students, professionals, or both, and we all share an interest in the new forms of higher education emerging and being discussed on the Net—hence, our attraction to #rhizo14/15. Particularly relevant to our current writing projects, we have all mastered the art of academic research and writing that is required for academic success. Some of us even teach research and writing to our own students. Thus, the new kinds of writing emerging on the Net are of keen interest to us professionally and personally, and our curiosity must be framed within the context of our professional work.

Next, we are all competent or better users of modern information technology. If we haven't used a particular tool that interests the swarm, then we are all adept at mastering the new tool in short order. Google Docs, the tool that we have focused on the most, was an easy decision for all of us, and we were all able to push it to its technical limits.

We need to talk about our swarm memory, such as we can remember it. And this brings me to a final point about memory in swarms: memory is distributed and not necessarily evenly. Thus, no one member of the swarm has all the memories. Even Google Docs, which records and date/time stamps every key stroke, does not have any memory of the tweets, texts, and Facebook messages among the humans, nor of their readings and research. Yet all of this memory is necessary for a full understanding of the identity of the swarm and of the documents that it has produced.

Monday, April 2, 2018

RhizoRhetoric: 4 Open

I just realized that I merged two characteristics of complex systems in yesterday's post. Cilliers' third characteristic has to do with direct and indirect feedback loops, but I won't correct my mistake here. I think I covered it sufficiently yesterday.

Cilliers' fourth characteristic reads this way:
Complex systems are open systems—they exchange energy or information with their environment—and operate at conditions far from equilibrium.
In my previous post, I explored how complex systems exchange energy and information among the elements within the system. This is the complementary process in which the system exchanges energy and information with its ecosystem. In some ways, this is the external process that feeds the internal process. A complex system (think here of a zygote or a writing swarm) may bring an internal store of energy and information (think of DNA and college degrees), but without regular exchanges of energy and information from the ecosystem, any complex system will die. Again, this is a fundamental process that accounts for the formation and functioning of stars and atoms as well as of writing swarms.

One cannot understand a writing swarm without understanding the energy and data streams that feed that swarm. The identity of the swarm emerges as the nexus of all those internal and external flows and exchanges of energy and information, and the swarm functions and sustains itself only so long as flows and exchanges persist.

The energy and information flows are dynamic, constantly evolving, which means that the complex system such as a writing swarm operates "at conditions far from equilibrium." Direct and indirect feedback loops are at work between the swarm and its environment just as they are within the various elements of the swarm itself. New information and energy feeds into a swarm, which processes that energy and information, dampening some and amplifying other but all the while modifying itself to accommodate and respond to that new data, and then the swarm feeds back new information and energy, which modifies its ecosystem across all scales. Constant feedback cycles process energy and information and feed back energy and information at both local and global scales.

This processing is at times synchronous within a writing swarm, but more often it is asynchronous as different writers across the world work and write at different times, so that documents emerge in fits. A whole section can emerge in a somewhat new direction with new energy and information, and then the entire swarm must process that new direction and adjust itself to it. Sometimes the new section finds a home, sometimes it is pruned. It is always edited and always leads to other edits as the swarm reconfigures itself.

This network of dynamic exchanges within and without the writing swarm defines the swarm and the writing that it produces. Eventually, the exchanges of energy and information stop and the document attains equilibrium. It is completed. The writing swarm, however, does not attain equilibrium; rather, it remains dynamic if it does not disband or die. Tension can emerge between the static, completed document and the dynamic, constantly reforming swarm, which can move beyond or away from the document in a very short time.

Swarm writing, then, can be a very messy process. While the swarm will try to manage its internal processing, it is always open to new information and energy streams which can surprise, delight, and confuse the swarm. Swarm writing makes great demands on the resourcefulness and resilience of the swarm to find its place within its ecosystem. This has certainly been the case with our swarm, which has struggled to find a way to express itself within an ecosystem not quite ready to listen to its song.

Friday, March 30, 2018

RhizoRhetoric: 2 & 3 Rich Interactions

Paul Cilliers' article "What we can learn from a theory of complexity" posits seven characteristics of complex systems, and I'm exploring each of the seven to illuminate the concept of swarm writing. Like Deleuze and Guattari, Cilliers begins with the characteristic of multiplicity, which I explored in my last post. In short, a complex system is composed of multiple elements at multiple scales.

In this post, I apply to swarm writing Cilliers' second characteristic of complex systems, rich interactions:
The elements interact dynamically by exchanging energy or information. These interactions are rich. Even if specific elements only interact with a few others, the effects of these interactions are propagated throughout the system. The interactions are nonlinear. There are many direct and indirect feedback loops.
This single characteristic could easily be parsed into half a dozen characteristics, but I think the main idea for me is that the emergence of a complex system such as swarm writing depends upon the ability and tendency of various elements to interact with one another. Prior to this rich interaction, a collection of elements is no more than a random scattering of people with no connection and no interactions with one another. Not until these elements—say, a group of people who happen to share a MOOC—begin to interact can a complex system, such as swarm writing, emerge. The coherence and identity of the swarm emerges from the rich interactions among the elements. The entity that emerges—in our case, a swarm—functions and has its identity at a scale different from its individual elements.

The network of interactions within a complex system is kickstarted by the exchange of energy and information, and in the case of swarm writing, mostly by the exchange of information—though I think most of my fellows in the swarm will agree that we exchanged lots of energy as well. Flows of energy and information inform all complex systems, and this process connects the emergence of writing swarms with the emergence of stars, galaxies, earths, life, nation states, the Internet, and love affairs. This flow of energy and information is about as fundamental a process as humans can imagine, and I find it gratifying to imagine a rhetoric that begins at the same place as the Universe.

I think, then, that our document about swarm writing should say more about the exchanges of information and energy that informed the emergence of our particular swarm. I believe this would clarify for recalcitrant readers what we are trying to say. I can begin a list of the information and energy that I know about, but I really need my swarm here. Flows of energy and information are much like the flows of streams that feed a river: they come from a thousand different sources and take a million different paths. I cannot know all of them. Even my swarm cannot know all of them, but they can know enough of them to give a sense of the complex ecosystem that we are trying to describe. We should do this.

Then, the interactions among the actors in a swarm are mostly local, but because they are nonlinear with many direct and indirect feedback loops, they can propagate throughout the swarm. This makes great sense of any swarm, writing or otherwise. For instance, I interacted mostly with a handful of people and mostly through Facebook and Google Docs. Others in the swarm interacted with a different group of actors, similar to mine, but not the same (note the fractal flavor here). Moreover, my local, immediate connections morphed from the beginnings with #rhizo14 until this latest version of the swarm writing this document.

Like all the different actors in the #rhizo14 swarm, I brought certain ideas and energy to the group mostly communicated to my immediate, local connections. Those connections then muted some of my ideas, or amplified some. The amplified ideas propagated to the rest of the #rhizo14 swarm, perturbing the group in various ways, changing the flavors of its interactions and identity. In communication terms, I was helping to develop and re-channel the conversation, as were other actors in the swarm. These complex set of interactions are what made #rhizo14 and its sub-swarms what they are. I think we need to say that in our current essay. We can at least hint at it, providing a general sketch.

Then, note the interactions between the different scales of the swarm. The entities that function at different scales—the total #rhizo14 swarm, the sub-swarms, the individuals—are not insulated from each other. Rather, energy and information are exchanged across scales in direct and indirect feedback loops that inform and modify each other. Each scale feeds on the other scales, modifies its own internal structures and functions, and then feeds back to the other scales, which modify in turn, around and around in a mostly elegant swarm.

Finally, the nature and identity of the swarm emerges from the interactions among the actors, not from the individual characteristics—such as intelligence or beliefs about education—of the individual actors. The documents that emerge from swarm writing are a result of the network of rich interactions among the actors. The documents are the result of non-linear mathematical operations, not the result of simple linear addition or subtraction. Thus, small events such as using Slack or not can lead to big effects. Similarly, large events such as the failure of #rhizo16 to actually convene can lead to small effects. Moreover, the interactions are not all nice. Our writing swarm did not simply hold hands and sing Kumbaya. Interactions are at various times cooperative, collaborative, and competitive. We challenged each other, usually courteously, but not always. We should discuss this in our paper.

I think our paper will benefit from a brief description and discussion of the rich interactions in our swarm, and I challenge our group to consider it.

Thursday, March 29, 2018

RhizoRhetoric: 1 Multiplicity

I'm working with a #rhizo15 group to revise a document we've submitted for publication in a scholarly journal. The document tries to map the swarm writing that emerged from the Rhizo14/15 MOOCs, and I think our problems are two-fold:
  1. We find it difficult to demonstrate and discuss open swarm writing within the narrow boundaries of a traditional scholarly study, and
  2. We find it difficult to connect readers with a traditional research agenda to the concept of swarm writing.
One sympathetic reviewer suggests that we focus more on swarm writing, and I want to explore some ideas in the next couple of posts. One of the personal problems I have in revising this particular document is that my own thinking about swarm writing has developed much since we first wrote this document, and I find myself wanting to add new material to the document rather than merely editing the existing material. I'll try the new material here first rather than in the document to see how it wears.

I'm starting with an article about complexity from Paul Cilliers entitled "What we can learn from a theory of complexity" first published Mar 31, 2000, in Emergence: Complexity and Organization. I'm drawn to this particular article from the many I've read by Cilliers because, paradoxically, of its simple list of some relevant characteristics of complex systems. I consider swarm writing a function of a complex system, and I think these seven characteristics will help our article address the problems we have with discussing swarm writing in a traditional scholarly context.

Cilliers begins his article by listing seven characteristics of complex systems:
  1. Complex systems consist of a large number of elements that in themselves can be simple.
  2. The elements interact dynamically by exchanging energy or information. These interactions are rich. Even if specific elements only interact with a few others, the effects of these interactions are propagated throughout the system. The interactions are nonlinear.
  3. There are many direct and indirect feedback loops.
  4. Complex systems are open systems—they exchange energy or information with their environment—and operate at conditions far from equilibrium.
  5. Complex systems have memory, not located at a specific place, but distributed throughout the system. Any complex system thus has a history, and the history is of cardinal importance to the behavior of the system.
  6. The behavior of the system is determined by the nature of the interactions, not by what is contained within the components. Since the interactions are rich, dynamic, fed back, and, above all, nonlinear, the behavior of the system as a whole cannot be predicted from an inspection of its components. The notion of “emergence” is used to describe this aspect. The presence of emergent properties does not provide an argument against causality, only against deterministic forms of prediction.
  7. Complex systems are adaptive. They can (re)organize their internal structure without the intervention of an external agent.
I want to write seven posts applying each characteristic to swarm writing to see if this can help me illuminate swarm writing for those who see it as little more than standard writing in a Google Doc.

Cilliers' first characteristic involves a multiplicity of interacting parts, as I see it: "Complex systems consist of a large number of elements that in themselves can be simple." This first characteristic drives to the heart of what makes swarm writing different from the long tradition of Western rhetoric: swarm writing undermines the idea of a single, or at least unified, authorial voice usually declaiming on a single, unified topic. This also pinpoints the issues the #rhizo15 swarm is having with our document: we struggle to create a single, unified voice addressing a single, unified topic to meet the demands of traditional scholarship. In other words, we are trying to describe an open, dynamic process in a closed, rigid scholarly document.

So let me restate Cilliers in terms of swarm writing:

Swarm writing consists of a large number of actors (human and non-human) that in themselves can be simpler.

Note my changes. First, I say actors rather than elements to reflect my belief that everything in a complex system has agency. I could have used the term agent, but we use actor network theory (ANT) in our document, so I'm using actor. Also in keeping with ANT, I'm including all actors: pens, pencils, paper, Google Docs, smartphones and tablets, global communication networks, college educations, and Becky, Maha, and AK—most all the actors that play in our writing swarm, human and non-human.

Also note that I change simple to simpler to reflect my belief that most everything is complex. Consider the elements of our swarm that I list above. Perhaps a pencil is simpler than a human, less complex. Still, I see a pencil as a complex system. If you take the ANT approach to understanding a pencil, then you quickly see how the simple pencil becomes complex. A finished pencil emerges at the end of incredibly rich and complex mining, manufacturing, and marketing systems that interact with countless other actors, and then that one pencil takes a path through the world that can be incredibly rich, even unique among all pencils. For me, then, simplicity is a relative state, not an absolute characteristic.

I also should add here that while it is easy to see a million people tweeting #MeToo as swarm writing, I also think that a single person writing—me writing this current post, for instance—is swarm writing. I, too, am a swarm—no doubt a less complex swarm than my #rhizo15 swarm or the #MeToo swarm, but a swarm none the less.

Still, the way Cilliers phrases this first characteristic lays the groundwork for his later comments about emergence. As a large number of elements or actors begin to swarm together, a more complex actor can emerge: the complex system, or a writing swarm, or a novel, a nation state, a galaxy. This emergent actor is usually more complex than its constituent actors and can function at another scale.

But much needs to happen before a collection of people and their tools become a writing swarm. That's what the next characteristics and posts are about.

Friday, January 26, 2018

#MeToo: The Post-Humanity of Rhizo-Rhetoric

So is #MeToo expanding rhetoric beyond the humanist focus on the individual to the posthumanist inclusion of the human/nonhuman swarm and ecosystem? I think so.

But let me point out that this expansion does not dismiss the individual and her unique experience. Each individual experience has its own meaning, and our traditional rhetoric gives us a rich set of tools for reading that experience. I am exploring the idea, however, that a different constellation of meaning emerges at the hyper-text scale of #MeToo, and that I, at least, don't have the set of tools required for reading that text. Somewhere George Siemens said that "literacy is the ability to engage in the dominant discourses of the current age." #MeToo is one of the dominant discourses of the current age, and I think I'm missing a big part of it. I suppose that makes me illiterate, so there is plenty of room here for me to learn. Cool.

Byron Hawk claims that Deleuze captures this shift in rhetoric from the simple individual human to the complex posthuman swarm in his concept of expressionism, which Hawk opposes to expressivism. According to Hawk, expressivism "is centered on the individual body" and leads to a social-epistemic rhetoric that "is centered on a dialectic among distinct, pre-existing elements in the world" (158). This rhetorical focus on human individuals in dialectical opposition to each other leads to a rhetoric that operates "from an opposition between human intention as active and material context as static and passive, thus privileging human action" (158). Expressivism rather unconsciously assumes traditional rhetorical elements and arrangements: humans engaging other humans through texts for the purposes of persuading, informing, and entertaining. Humans are the only actors on stage, and everything else is inert scenery and prop, interesting mostly as a backdrop to human agency. Much of the discussion about #MeToo has assumed this arrangement and focused on political and social positions assumed by various people, winners and losers in the discussion, and probable outcomes and consequences for people.

Hawk says that Deleuze undermines this focus by "seeing any body, organic or inorganic, not as a whole but as a constellation of parts that participate in multiple systems" (158). From this point of view, a text "can be only the expression of a world, of an entire system, of life, not just one element or function within it" (158).

#MeToo, then, is the expression of the world, of an entire system, not just one element within it. I cannot think of #MeToo as a collection of individual tweets, a bag of marbles. Yes, I can look at the bag of marbles and, for instance, divide them into big ones and littles ones, or red, green, and blue ones. I have the rhetorical and analytical skills to do that, and I will generate some useful knowledge that way, but if I exclusively focus on #MeToo at that scale, then I miss the larger and, for me, the more important text. #MeToo is the world struggling to understand how it organizes and interacts with itself, especially in terms of the relationships between women and men.

This larger text is problematic for other readers as well. In her article for The New Yorker entitled "The Rising Pressure of the #MeToo Backlash", Jia Tolentino explores the tendency of people (men and women both) to flatten #MeToo into a single, simple text with a single, simple message which they can then position themselves for or against, just as the traditional rhetorical strategies have taught them to do. Tolentino complains that this strategy undermines the real power of #MeToo:
This is an unprecedented moment of flux on an impossibly complicated topic; this movement is not even three months old yet. The fact of a hashtag flattens these stories, makes them seem unified, but they are profoundly individual. If we stop looking for straightforward collective agreement, we might find we need it less than we think.
I, too, must stop looking for straightforward collective agreement. I really don't need it. Instead, I'm trying to see #MeToo as a fractal: a swarm of self-similar and coherent pieces but not identical pieces. #MeToo has a swarm message that we want to reduce to a simple, political slogan, and in many ways, it doesn't matter if the slogan supports or opposes #MeToo. While they have utility in narrow applications, all slogans undermine and distract us from the swarm message.

But we so much want attractive slogans delivered by attractive voices. An Ozy story "Aly Raisman Is the #MeToo Hero that American Sports Needed" by Nick Fouriezos follows the typical rhetorical strategy of reducing the 156 witnesses in the trial against women's gymnastic physician Larry Nassar to one attractive voice: Aly Raisman. This is traditional Western journalism based on traditional rhetoric that gives us a single, identifiable voice to deliver a single message that we can agree with, disagree with, or ignore. Fouriezos' strategy is quite obvious. He characterizes the testimonies of 156 witnesses (a swarm) as "a public bloodletting only made possible by the decision from Judge Rosemarie Aquilina to give every victim the voice they had been denied." His treatment of Aly Raisman, however, is largely positive. Her voice is clear, courageous, and convincing. And her voice is clear, courageous, and convincing—make no doubt—but so are the stories of the other 155 athletes. Fouriezos mostly misses that swarm text, as do his readers. I want this larger, swarm text, this hyper-text.

Or let's call it a rhizo-text to match rhizo-rhetoric. Let's see if rhizo-text works.

To approach this larger rhizo-text, Hawk uses Heidegger's discussion of tool to reconceptualize technê, especially as handicraft, or craft or art, in rhetoric. For Hawk, technê is not something that a human does to a text through the use of various tools (pens, paper, typewriters, word processors, Twitter, etc); rather, technê is poiesis, "the arising of something from itself, … a bringing forth" (176). Technê is a constellation of agents that includes humans but does not privilege humans. Hawk says:
Technê as handicraft or as rhetoric and poetics is set in the context of physis: nature, the ecology as a whole, including humans, is the ground and thus highest form of technê, which is simply one aspect of co-responsibility. This recognition is a key to moving beyond instrumentality and humanism. Heidegger asks, “Does this revealing happen somewhere beyond all human doing? No. But neither does it happen exclusively in man or decisively through man” (["Question Concerning Technology"] 24). A human does not create by itself. It enters into a situation, and the new form taken by that constellation plays out its own potentiality. (176)
So I begin to see #MeToo rhetorically as millions of agents (in this case, overwhelmingly women and Twitter) entering into a new situation arising from their own interactions. The text written by this new constellation—this #MeToo—is playing out its own potentiality, which arises from its own DNA, its own experiences, knowledges, skills, and trajectories, and equally from the ecosystem within which it finds itself.

It's easy for those of us who engage #MeToo to credit only the humans who are writing it, but we miss much if we do not also credit Twitter and other social media. #MeToo could not have emerged at all and it would not be emerging as it is without nonhuman agency. #MeToo, then, is a posthuman document, as Hawk explains using the work of N. Katherine Hayles':
Hayles characterizes posthumanism as locating thought and action in the complexity of distributed cognitive environments. … For Hayles, “modern humans are capable of more sophisticated cognition than cavemen not because moderns are smarter, . . . but because they have constructed smarter environments in which to work” (How We Became Posthuman 289). Posthumanism does not usurp the human, then, but situates it in the development of distributed cognitive environments. Hayles writes, “No longer is human will seen as the source from which emanates mastery necessary to dominate and control the environment. Rather, the distributed cognition of the emergent human subject correlates with—in Bateson’s phrase, becomes a metaphor for—the distributed cognitive system as a whole, in which ‘thinking’ is done by both human and nonhuman actors” (290). (176, 177)
This reorganizes our conception of rhetorical voice as the expression of a single human or group of humans exercising her or their will upon a text and thereby upon another group of humans. That view, though at times useful, is too limiting. Rhizo-texts are written rhizomatically and must be read rhizomatically. Deleuze and Guattari address this very issue in the first paragraph of the first chapter of A Thousand Plateaus:
The two of us wrote Anti-Oedipus together. Since each of us was several, there was already quite a crowd. Here we have made use of everything came within range, what was closest as well as farthest away. We assigned clever pseudonyms to prevent recognition. Why have we kept own names? Out of habit, purely out of habit. To make ourselves unrecognizable in turn. To render imperceptible, not ourselves, but what makes us act, feel, and think. Also because it's nice to talk like everybody else, to say the sun rises, when everybody knows it's only a manner of speaking. To reach, not the point where one no longer says I, but the point where it is no longer of any importance whether one says I. We are no longer ourselves. Each will know his own. We have been aided, inspired, multiplied. (3)
They are trying to explain here that the two of them are a swarm and must be read as a swarm, and they make clear that their swarm includes not just other people but other things, non-humans. They proliferate and run like oil on pavement, or seeds in a stream, like rhizomes.

And here is a point I must address: a single writer is a swarm, just as a million writers are. In this #MeToo series of posts, I've been contrasting the million voices of rhizo-rhetoric to the single voice of traditional rhetoric. This is misleading. A single voice is also a swarm, but it's easy to see how traditional rhetoric could create a fictional unity of that voice.

So even if this unified voice is a fiction, what's wrong with that? In some senses, nothing is wrong with it. It is, if nothing else, convenient. As D&G wryly note, they use their own names—unifying, signifying labels—"purely out of habit … because it's nice to talk like everybody else, to say the sun rises, when everybody knows it's only a manner of speaking." And yet, everything is wrong with it. These signifying labels, these fictional singularities, "prevent recognition [and] render imperceptible, not ourselves, but what makes us act, feel, and think." Thus, the Fouriezos article gives us a unified voice in Aly Raisman, but it prevents recognition of and renders imperceptible the 155 other voices. It gives us a text rather than the rhizo-text. It gives us the human rather than the post-human.

Let me end this post where I began it by noting that I am not denigrating the human or humanistic rhetoric. This is the tradition I was trained in and have practiced for most of my life. The humanistic tradition has its profound insights, its utilities, its affordances, and its limitations. All my posts in this blog have been generated from that rhetorical perspective and can be read from that perspective. So I am not denigrating; rather, I am expanding. In part, I am expanding because texts such as #MeToo force me to confront their rhizomatic nature while traditional articles, essays, poems, and posts do not—even though their rhizomatic nature is present. I see a thousand plateaus before me, and I want to walk there awhile.

Thursday, January 25, 2018

#MeToo: The Ecology of Rhizo-Rhetoric

I'm examining the new kinds of documents that I see emerging on the Net, and I'm focusing on #MeToo, as it is currently the strongest and most visible of the kinds of documents I'm thinking about, but it certainly isn't the only one. In my last post, I looked at #MeToo as a hyperobject as Timothy Morton defines them. In this post, I want to begin thinking of #MeToo in terms of rhetoric. After all, I'm defining #MeToo as a text, and rhetoric should have something to say about any text.

As I recall from my years of reading rhetoric, most rhetoricians do not approach writing and communication from the view of complexity. Most in fact try to reduce writing to the simple or complicated domains, with fairly simple models and heuristics for producing useful texts. However, by the late 20th century, rhetoricians and literary theorists were beginning to push rhetorical thought into the complex domain. For instance, in his book A Counter-History of Composition: Toward Methodologies of Complexity (2007), Byron Hawk traces the emergence of complexity in modern thought, particularly in modern rhetoric, through the concept of vitalism. For Hawk, vitalism begins with Aristotle, takes a turn in rhetoric toward Romantic expressivism, and eventually flowers in the 20th century through the science and philosophy of complexity. Hawk does an admirable job of showing how a rich concept can influence science, philosophy, and rhetoric and how each of these disciplines can feed into and off of the other. Even if you are not so interested in rhetoric, his argument illuminates the history of an idea the informs much of modern thought.

I don't intend to recount Hawk's book, but I do want to explore several of his ideas in terms of #MeToo. The first idea is that complexity rhetoric, or what I have called in previous posts rhizo-rhetoric, is ecological rather than atomistic, or complex rather than simple.

Hawk starts his discussion of vitalism with Aristotle's concept of entelechy, which seems to lay the intellectual groundwork for ecological thinking for Hawk. For Hawk and Kenneth Burke, whom Hawk quotes, entelechy is "essentially a biological analogy. It is the title for the fact that the seed ‘implicitly contains’a future conforming to its nature, if the external conditions necessary to such unfolding and fulfillment occur in the right order. Were you to think of the circumstances and the seed together, as composing a single process, then the locus of the entelechy could be thought of as residing not just in the nature of the seed, but in the ground of the process as a whole" (Burke, The Rhetoric of Religion, 1961, pp. 246-247). Entelechy, then, embeds both seeds and rhetoric into a complex ecology, into a rhizome, and this idea that all things unfold through the interaction of internal resources and external environments begins to lay some groundwork for me to understand #MeToo.

To read #MeToo, then, I must frame it in a complex ecology, a rhizome, that considers both the text itself and the ecosystem of the text as a single process. #MeToo, of course has its internal resources, its DNA, arising from the experiences of millions of women and men and their abilities to express those experiences, but the unfolding and unpacking of that DNA happens within an ecosystem that seeks to express its own DNA and that may or may not support #MeToo. The environment is a co-creator of #MeToo, and this is made very clear when we learn that the MeToo meme was actually created a decade ago by a black activist named Tarana Burke who, according to Ebony magazine, started MeToo "as a grassroots movement to aid sexual assault survivors in underprivileged communities 'where rape crisis centers and sexual assault workers weren’t going.'” I don't know why #MeToo emerged now instead of 10 years ago. Perhaps because Tarana Burke is not a well-known movie star with thousands of Twitter followers. Maybe because Twitter was just created a decade ago and was not yet the force in social networking that it has become. Perhaps those reasons and many more, but the main point for me is that ten years ago the environment was not right for #MeToo. The #MeToo seed had fallen on dry, barren ground, and it did not emerge even though there were just as many millions of women who could speak to the issue. In 2017, the seed fell into fertile soil and sprouted. Then exploded.

Hawk says of this ecological frame for rhetoric:
The basic logic of entelechy is that the overall configuration of any situation, including both natural and human acts and forms, combines to create its own conditions of possibility that strive to be played out to completion. The combination of the four causes in nature is not just a push from behind but also a pull toward the future, the striving to develop potential. In more contemporary evolutionary terms, an ecological situation produces the structural conditions for certain types of plants or animals to develop and thrive and they strive to fill those gaps, to enact that potentiality. Humans as an efficient cause cannot be abstracted from this larger contextual ground set up by the other causes and the ecology or potentiality they enact. A human might have an internal, psychological, or intellectual motive, but a huge variety of cultural, linguistic, and material factors help create and enact that motive. As part of nature, humans can help realize the situational potential via the technê available to them through the complex ecological arrangement, and it is in this larger movement that rhetoric operates. (126)
First, note that an ecological understanding of rhetoric undermines the traditional concept of writing as an individual who through innate knowledge and skill creates texts that engage others, usually to meet some purpose of the writer, some need of the reader, or some issue in the world, or all three. Writers write to act on the world and to make things happen, and if I consider individual tweets and texts, then this can be a useful frame for thinking about #MeToo. For instance, consider Alyssa Milano's original tweet that kicked off the current #MeToo text:
I can describe this single tweet as Alyssa Milano writing a message to persuade her followers to express their own sexual harassment and the extent of sexual harassment in our society. Obviously, millions did, and the #MeToo text emerged and morphed around the world, far exceeding Milano's expectations. But this original tweet is neatly captured and usefully illuminated by a traditional rhetorical analysis that frames the communication as a writer writing to some reader about some issue to make something happen. You can easily model this with the communications triangle that I use in my college writing classes:
A writer, a reader, a subject, all joined by a text—in this case, a tweet. You can even change the terms to pull from different strains of communication theory:

I do not dismiss the immediate, though limited utility of framing writing like this, but for me, this frame is woefully inadequate for reading and understanding #MeToo. It's first problem is scale. It is too focused on the single tweet from Alyssa Milano, the single artifact of a single writer. #MeToo is much bigger than this one tweet. If this was the only tweet in #MeToo, then we would have no #MeToo, and I would be looking at other hyper-documents. I'm discussing #MeToo because it is a swarm of millions of tweets, texts, messages, articles, television discussions, and acceptance speeches that push #MeToo into the higher scale of hyperobjects. This higher scale makes #MeToo interesting and gives it its power, and the single Milano tweet is interesting to me only because of this higher level text. Indeed, I did not follow Milano on Twitter at the time of her tweet, so I would never have seen it had #MeToo not emerged.

The communications triangle also focuses too much on the individual writer. Yes, Milano wrote the first tweet in October 2017, and her voice is an integral, necessary part of #MeToo, but it is hardly sufficient to account for or to embody #MeToo. Milano's voice has been subsumed by the general hum that is #MeToo. While focusing on a narrow instance of #MeToo can be illuminating, it is ultimately distracting. Researching the behavior of a single neuron in the human brain can reveal much, but it doesn't reveal mind or consciousness, both of which emerge at a hyperscale above the single neuron. This is the scale at which I become aware of #MeToo, but traditional rhetoric inadequately frames this scale for me. I need a larger frame, a more ecological frame.

Hawk looks to artificial life studies to expand the frame of rhetoric from the single writer to a swarm of agents, both human and not:
In a paper delivered in 1992 at the third Workshop on Artificial Life, Mark Millonas wrote, “The notion that complex behavior, from the molecular to the ecological, can be the result of parallel local interactions of many simpler elements is one of the fundamental themes of artificial life. The swarm, which is a collection of simple locally interacting organisms with global adaptive behavior, is a quite appealing subject for the investigation of this theme” (quoted in Mark Taylor 153). … Essentially the study of life through artificial means extends the shift from examining characteristics of living beings to examining functions of living systems. (156, 157)
For me to read #MeToo, then, my rhetorical strategies must shift from framing the "characteristics of living beings", or individual writers, to framing the "functions of living systems", or swarms of writers. I have to see a complex, powerful document such as #MeToo as emerging from "simple locally interacting organisms with global adaptive behavior".

Many may object to characterizing the millions of people, mostly women, who wrote #MeToo as "simple locally interacting organisms with global adaptive behavior". It sounds demeaning—like ants in a pile—but I think that is a trick of scale.

Each individual #MeToo writer is simpler than #MeToo in two ways. First, they are simple in comparison to the complexity of the larger scale document that emerged from the aggregation of each tweet. An individual ant is a complex, capable creature at its own scale, but it is more simple and less capable in comparison to the ant colony. Likewise, the #MeToo writers are complex, capable people at the human scale, but they are more simple and less capable than the hyper-human scale that #MeToo works in. No single #MeToo text, not a tweet from Alyssa Milano or a speech from Oprah Winfrey, can match the power and force of a million tweets. #MeToo is a hyper-text that functions at a scale above the human, a hyper-human scale, a cyborg scale, and no individual human can measure against it.

But there is no need to measure against it. This is not an exercise in comparison, and it certainly isn't a denigration of individual people; rather, #MeToo is a celebration of the kinds of powerful texts emerging in a posthuman world.

Then each #MeToo document (tweets, text messages, posts, and more) is simpler than the #MeToo text. This is especially true of individual tweets. Most of the tweets are simple responses to Milano's call to retweet, and each can be characterized in simple terms of a stimulus-response as we might characterize the firing of a neuron in response to some stimulus. The responses, in turn, stimulate what becomes a cascade of responses across Twitter, and #MeToo emerges. I do not dismiss the benefits of learning about an individual stimulus-response pattern. Understanding stimulus-response is necessary for understanding the functions of the brain, for instance, or for understanding Twitter, but it is insufficient for understanding consciousness or #MeToo, both of which emerge at a scale beyond stimulus-response and create characteristics not inherent in the stimulus-response pattern. Mapping the trajectory of a single stimulus-response is enlightening and helpful, but it is not sufficient to map the trajectories of millions of stimuli-responses. One thing happens when a single neuron fires, but something else altogether different happens when millions of neurons fire.

Reducing #MeToo, or mind, to a single stimulus-response is both misleading and denigrating. First, it denigrates by denying the validity or even the possibility of emergent properties at the hyper scale. Many will deny the possibility of meaning emerging at the hyper-human level of #MeToo, and will insist that #MeToo is no more than, at best, a group of concerned women expressing their individual outrage or, at worst, a group of liberal fanatics bitching and moaning about not much. Even though the first opinion supports #MeToo and the second attacks it, they both undermine the real power of #MeToo which emerges at a scale above the individual.

Then, reducing #MeToo denigrates by glossing over the complexity of the individual interactions that we render simple. For instance, the term stimulus-response treats the complex behavior of an individual neuron as if it's no more than one billiard ball bumping into another. A single neuron firing is itself a complex event within a complex ecosystem. An individual neuron is a network of many parts, and it has the intelligence to collect data from its environment, to assess that data, and to change its internal state to respond to that data. It has many of the same complex characteristics at the neuronal scale that we humans have at the human scale. In other words, a neuron only looks simple from the great remove of the human scale. As Olaf Sporns proves in his 2010 book Networks of the Brain (not network singular), a neuron firing is a damned complex network in its own right, and we misunderstand it if we treat only as a simple mechanism. Likewise, a single woman retweeting #MeToo is a damned complex network in her own right. She is not just one more woman championing or complaining in a tweet but a complex constellation of experiences and knowledges, some too deep for words. To reduce these women to a simple group or to a simple response misleads and undermines our understanding of #MeToo.

This ecological approach drops both Hawk and me at the doorstep of post-humanism, which I will discuss in a next post.