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?

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.

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.

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.

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.

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.