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A slide deck discussing Chapter 1 of my book Network.

A slide deck discussing Chapter 1 of my book Network.

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  • Here, we’ll discuss some of the concepts in Chapter 1 of my book Network. Ch.1 introduces the notions of network and genre, and it introduces 4 cases within a heterogeneous organization. Yes, the word “genres” is in red here. Even though Network Ch.1 talks about networks and genre, we’ll discuss networks in the other slide decks. Today, let’s focus on genre, which is not as well defined in the book but which is in itself a very useful way of looking at people’s interactions.
  • So here’s what we’ll get out of this slide deck.I should mention that today is going to be lecture-heavy, with some exercises thrown in. Later lectures will involve more interaction.
  • A “text type”
  • Writing is the most flexible tool we have, and in a highly literate society, when we encounter a problem, we tend to reach for a textual solution. When we encounter a recurrent problem or situation, we tend to reuse the textual solution we used for the previous version of the problem. Over time, these responses become typified. That is, we face a familiar problem, and we reach for or create the text that offers a familiar solution.The more typified these texts are, the easier it is to share them with others who face similar problems. Over time, some of these genres develop and become more defined, more rigid, and more controlled. As an instantiated solution, they also embed a particular viewpoint and logic. As we’ll see in a moment, this isn’t always good, especially when genres must bridge between two different viewpoints, logics, or cultures.Importantly, genres are also “tools-in-use.” That is, genres are interpreted by their authors and their audience – and sometimes those interpretations are very different. We can’t understand genre just from looking at the form of a text.These characteristics help genres to weave together heterogeneous networks, since they provide regular ways for people to solve information problems. Used well, genres can bridge between familiar and unfamiliar activities, allowing people from different backgrounds to share and transform information properly. But genres also have weaknesses, since they involve different logics and worldviews. What’s more, as we’ll see later, people add more genres all the time – and these genres don’t necessarily share the assumptions of other genres in use, causing disruptions.Let’s get to some examples.
  • Let me take a moment to address something before I go on. When we hear the term “rhetoric,” we often think of lying, exaggerating, or manipulating. But that’s not what I’m discussing here. In fact, I am a professor of rhetoric, and I can assure you I don’t teach my students to lie. (And I hope you can believe me!)Rhetoric is the study of how people instruct, inform and persuade each other. That is, it’s purposeful communication. So when I discuss genres as rhetorical responses to recurrent situations, I mean that they respond to a situation in which people are trying to inform, instruct, or persuade each other. Or even themselves. Let me give you a fairly trivial example.
  • As noted,genres are relatively stable responses to recurrent situations. Usually these are instantiated in texts or speech. Since they're regular and recurrent, we can recognize them, share them, and use them in predictable ways. They're a way to mediate our own work and the work of others. Consequently, examining genres tells us a lot about how people understand their work.For instance, when we talk about a shopping list, we have a basic idea of what it might look like, based on what it's supposed to accomplish and on how similar texts have looked in the past. Shopping lists tend to be very different, as you'll see if you watch people shop at a grocery store, but they are similar and predictable enough that strangers could swap lists and probably do a pretty good job of filling each other's orders. In fact, I grabbed these two shopping lists from a Flickr stream. Although they’re not in English, I recognized them immediately and could use them to shop. I also know how I would use them: look for the item, and when it’s in my shopping cart, cross it off. Shopping lists are an elegant solution, so even though they have many variations, the basic problem orientation and solution are clear.More specialized genres might be harder to understand and might take a bit more time to learn. Think about learning a new software interface or doing your taxes or filling out a travel request. Examples in the book include credit reports, F1 Notes, and orders for phone service. As activities become more complex and bridge more domains, the genres that mediate them tend to become more complex as well, and often more rigid. Importantly, in complex situations, genres also tend to multiply and interact. Photos:http://www.flickr.com/photos/ex-smith/4966648255/sizes/z/in/set-72157626903397039/http://www.flickr.com/photos/ex-smith/3797903855/sizes/z/in/set-72157626903397039/
  • But as I mentioned, genres are not simply structural. They’re not simply forms into which people pour content. That’s because people interpret genres in their own activities. Even in a form that you fill out – such as this tax form – you must be able to interpret the form, decide if you need to fill it out, and coordinate it with other genres of documentation. Just as faith without works is dead, genres without activity are not genres.
  • Genres are not media. No matter what Netflix says, “TV Shows” is not a genre. Neither are “email” or “tweets.” These are all media in which genres can be instantiated.For instance, a TV show might be a situational comedy or a police procedural – two very different genres. An email might contain a proposal or a poem.
  • Genres without activity are not genres. So think of a genre as a “tool-in-use,” and examine the ways in which people use genres as well as how the genre looks. Look for a shared response that allows mutual interpretation within an activity.For instance, here’s an interlinear Bible that raises the MacBook Air to just the right height. If we examine the Bible in isolation, just considering its structure, we will likely focus on features that are not relevant to its actual user: its organization, its index, its language. In other activities, these features might be meaningful. But in this activity, the most important features are its dimensions and how they interact with the laptop and the table. (I’m fairly sure you all have used texts in this way.) So as we consider genres, we can’t focus strictly on their features, their structure, or even their titles. We can’t rely on what their authors intended them to do. We must examine how people actually use and interpret them.
  • We can examine genre along at least two dimensions: Development and levels of activity. Here, I’m drawing from my previous book, Tracing Genres through Organizations. You can take a look at it if you like, but this is the heart of the argument.
  • Genre is well suited for examining development. In fact, the orientation to development is built right into the word: it comes from the same Latin root that anchors words such as gene, genealogy, and Genesis. In an analysis of genre development, we look for a typified text that has been developed to address a recurring situation.We see a bit of this in Network Ch.1 in the discussion of F1 notes. But to get a fuller view, let’s look at a different case. Let’s go deep here.(Wikipedia: http://en.wikipedia.org/wiki/Genre)
  • Here’s a case I examined in my book Tracing Genres through Organizations. What I like about the case is that we can clearly see how these genres develop over time – and how that development becomes really problematic in spots as people pull in genres from very different activities, with very different assumptions. In this case, I examined how traffic safety workers in Iowa used a database of traffic accidents. They used it in different ways: Police officers determined where to set speed traps and where to crack down on drunk driving; City and county engineers determined where to regrade roads or erect stop signs;Legislators used it to evaluate current laws and regulations and to propose new ones;The Federal Highway Authority used it to argue for highway maintenance funds.All US states collect traffic accident data. But Iowa was the first to manage those data with a computer system.
  • In fact, Iowa developed it accident location and analysis system in 1974, based on data it had collected since the 1960s. We’re going to take a look at how its text types – genres – evolved over that stretch of time. Watch these genres as we proceed, and you’ll see some really interesting development going on.
  • First, the underlying data.When an Iowan gets into a traffic accident that results in fatalities, injuries, or damages over $100, she or he must fill out a driver’s report. For accidents with injuries or fatalities, a police officer will also file a report. Both types of reports included information such as location, conditions, number of vehicles involved, type of accident, and a diagram and narrative explaining the accident. Locational parameters were expressed in terms of address (3417 Coy Street), intersection (the corner of Coy and Franklin), or landmarks near which the accident took place (ramp 167 on I-35 southbound).If the location was uninterpretable, the Iowa Department of Transportation (DOT) sent a map to the reporting party, who marked the proper location with an X and sent it back to the DOT.Copies of the reports were forwarded to the Iowa DOT. Once a year, the DOT would summarize and analyze these data by hand and present them in annual reports from the DOT and other related agencies. The DOT would then send the reports to various state agencies. By 1971, the Data Processing Division of the DPS regularly compiled nine statistical summaries (Wilbur Smith and Associates, 1972, p. 21-22). The reports included prose analyses, tables, bar graphs, and pie charts. Again, these were all compiled by hand.As systematized accident data became more widely used, in fact, workers came to want and expect more from them. It’s all very well to know general statistics about accidents at rural intersections throughout the state, but is a particular intersection more likely to have accidents than other intersections? What kinds of accidents? Under what conditions? Such questions could not be routinely answered under the labor-intensive pre-automation system. The need grew for more data. A hand-based system couldn’t provide it.
  • In 1971, the Iowa DOT commissioned a consulting agency (WSA) to develop the Accident Location and Analysis System (ALAS), a system running on an IBM 3090 mainframe. By 1974, it was in operation. Mainframe-ALAS introduced several innovations.One was the node-link system. To automate accident locations, WSA had to turn the roadway system into a form that a computer system could easily store and manipulate. In addition, the system had to work with the existing data collection tools: the drivers’ and officers’ reports, which located accidents by street address, intersection, or distance from landmarks. WSA assigned each county and city a two-digit designation number, then mapped all counties with a system of nodes: coordinates marking all intersections, ramp terminals, railroad crossings, grade separation structures, bridges, road ends, 90-degree turns, county lines, major signalized commercial entrances, and interchanges and other multiple node intersections.Now workers had to locate accidents in relation to the nearest nodes. This is a complicated system. All you need to remember is that they assigned arbitrary six-digit numbers to locations so that they could get accident location information into the mainframe. This was not a great solution, and it became a huge problem later on.To relate the locations to the node numbers, they developed node maps like the one above: roadway maps with numbers superimposed.
  • And if a traffic safety worker wanted to see accident history for a certain location, they would fill out a form like this one and mail it to the DOT. The DOT would then translate it into a punchcard, run the request, and print out a report to send to the requestor.Forms were difficult to fill out, especially because people would make transposition errors when filling them out. Similarly, reports were difficult to read, so the DOT had to train specialists to interpret them. The node maps, request forms, and reports were all hybrid genres that merged existing genres from accident location with genres from business computing. That is, they folded in two different sets of logic, two different orientations to problem-solving.Nevertheless, access to complex accident data was quicker, easier, and cheaper than it had ever been before. Workers could more easily access statewide accident records. Complex analyses were more feasible and results were more consistent. The centralized database reduced errors in accident reports and counts. Eventually, the DOT produced node maps for all cities and counties to help local officials better understand reported data. Report formats became more standardized, and the number of reports and applications of accident data analysis increased. By 1990 the DOT was regularly sending customized ALAS reports to all 99 counties, including data such as accident statistics on each intersection in the county, as well as filling specific requests from county engineering offices.
  • It was not until 1989 that the DOT updated this system for the PC era. An 18-year-old DOT intern developed a PC-based ALAS that had the same capabilities as mainframe-ALAS: the ability to read the existing state crash data, run various queries, and generate reports. Whereas mainframe-ALAS had cost millions of dollars to develop and run, PC-ALAS was developed in Moreland’s spare time and could be distributed freely to any and all who were interested.This system started as a workalike for mainframe-ALAS. In fact, if you compare this dialog box with the form in the previous slide, you can see the direct similarities. Genres that were once the paper interface for local workers became the on-screen interface, and became more customized. The DOT distributed PC-ALAS, data files, and node maps to local agencies, so agencies could process their own requests. This ability led to more complex analyses; it raised the bar on what could be considered a good analysis.Instant feedback made it practical to make multiple subsequent queries, allowing workers to form and test increasingly specific hypotheses “on the fly.”Workers began to generate reports for preliminary results which could be used for further refining queries. The reports were no longer simply end products. They also became more customizable.At the same time, workers received minimal training.
  • But here’s where the node-link system became very problematic. Workers had to find the location on a cumbersome map, read the six-digit node number associated with it, turn to the computer, and type it in. Predictably, they often encountered transposition errors.They developed various workarounds, mostly focused on helping them juxtapose the node numbers with the dialog box. Some people made photocopies of the map area so they could use a smaller, flatter piece of paper. One wrote the node numbers on a sticky note, then kept it in a folder so she could bring it out every six months to run that query; she would then put it on the frame of her monitor, bringing the numbers very close to the dialog box.
  • In 1996, a master’s student in Civil and Construction Engineering was working at the Center for Transportation Research and Education (CTRE). He realized that PC-ALAS involved an “arduous process” and decided to adapt a geographic information system (GIS) to perform the same duties. He converted the underlying node-link data into coordinate data, making it possible to display and query traffic accidents without (necessarily) typing in node numbers.Although workers could still run queries based on node numbers, GIS-ALAS made it easy for them to simply click on a node (represented as a glowing dot on a screen map) instead. Workers could also select a group of nodes simply by clicking and dragging the mouse appropriately. Better yet, workers could elect not to use nodes at all: they could select areas of interest on the map instead, such as actual accident locations. Finally, queries resulted in highlighted spots on the map.
  • GIS-ALAS also produced tabular descriptions descended from the reports of mainframe-ALAS and PC-ALAS.Unfortunately, the GIS provided very limited ways to display these summary sheets, making them hard to interpret. That is, these tables reproduced parts of the PC-ALAS reports, but they didn’t constitute entire reports.
  • GIS-ALAS had other problems. For instance, converting node data to GIS data was very problematic. The node map data had to be converted to coordinate data to be used in the GIS. But since the two representational systems were so different, the mapping was imperfect. Accidents showed up in cornfields. Additionally, workers were used to spot maps, in which they would put several pins in an accident location to represent the number of accidents. But if several accidents had happened in one location, GIS-ALAS would show them as a single dot – or rather, several dots that were superimposed, displaying as one dot. This confused users considerably.
  • Throughout this process, genres were developed by adapting existing forms. But representations and even logics didn’t mesh. Each hybrid genre tried to mesh at least two different activities and two different frames of reference, leading to disruptions.
  • This sort of genre development happens elsewhere, especially in automated genres. We don’t necessarily have to go so deeply to see it! For instance, LinkedIn is based on the genre of the resume and associated genres, such as recommendation letters.
  • Facebook was inspired by “face books” at universities, in which students’ photos and names are printed to help them learn each other’s names. We can think of lots of different examples here. For instance, email descends directly from internal memos – they even start with the same fields: to, from, date, subject. And they have the same optional fields: CC and BCC, which refer to “carbon copy” and “blind carbon copy.” In fact, although it’s common to see innovated texts, it’s rare to see completely new ones. Usually they’re modifications of existing genres that are imported into the new activity.
  • So that’s genre development. But we can also examine genres along a second dimension: levels of activity. Think of genre as functioning at three different levels: macro, meso, and micro.
  • Here’s the three different levels. A genre works on each level simultaneously.
  • At the macro level, think of genres as cultural-historical artifacts. They embed particular ways of thinking, problem orientation, logics, and assumptions. What are people cyclically trying to accomplish in their activities? How do they think about the problem? What have been their traditional constraints, and how have they shaped how the genre developed? The macro level is generally unconscious. When you see someone using a genre – say, reading a report – and you ask them what they’re doing, they’ll probably tell you, “I’m reading a report.” They probably won’t say: I’m incrementally improving the roadway system.The macro level is oriented to long cycles of activity, typically on the order of months, years, or longer. Finally, at the macro level, genres can encounter disruptions called “contradictions.” (This is a term from activity theory, which we’ll read more about later.) Think in terms of large-scale differences that build up across activities.
  • For instance, the contradiction between the node-link system and the roadmap built up over time, causing increasing conceptual difficulties. These increased with GIS-ALAS, which introduced a third conceptual system. The node map data had to be converted to coordinate data to be used in the GIS. But since the two representational systems were so different, the mapping was imperfect. Accidents showed up in cornfields. Sometimes multiple accidents showed as a single dot, which users couldn’t interpret.
  • We can see another example in Network Ch.1. In Case 2, Darrel, a sales representative, very much wants Gil in Credit & Collections to approve a new customer. Gil says no. Darrel and Gil are at cross purposes because they are engaged in different cyclical activities – signing up more customers vs. keeping out bad customers. This essential difference shows up in many ways: their incentive structures, their connection to the customer, their criteria for sizing up customers. But we can clearly see the differences come to a head in the genres at use in this case. Darrel is using a customer application, which focuses on things such as customer deposit, number of lines, and money that will be brought in each month; it predicts future interactions, assuming that the customer’s claims are true. Gil is using a credit report, which focuses on creditworthiness; it examines the past and assumes that future interactions will fit the same pattern.
  • At the meso level, think of genres as tools-in-use.These allow people to reach specific, conscious goals. When you see someone reading a report – and you ask them what they’re doing, they’ll probably respond on this level: “I’m reading a report.”The meso level is oriented to shorter timescales: minutes or hours. Think in terms of tasks or goals that they are using this tool to accomplish.Finally, at the mesolevel, genres can encounter disruptions called “discoordinations.” Think in terms of day-to-day difficulties in relating two or more genres together.
  • For instance, I mentioned earlier that people had trouble taking a six-digit number from the node map and typing it into PC-ALAS. They consistently encountered a range of errors, and they developed several workarounds to coordinate the two.
  • We can see a text functioning at this level in Network Ch.1. In Case 3, Customer Service has been using F1 notes – freeform database fields in Telecorp’s database – to record minimal information about customer transactions. At first, these tended to be minimal, only supplementing informal conversations. When they were used, they tended to vary widely, sometimes describing the worker’s actions, sometimes warning others that “this customer was not nice.” That is, the goal of F1 notes was unclear.But the manager of Customer Service decided to clarify the goal of F1 notes. He finally developed a script for F1 notes, made sure it was printed and taped to each monitor in Customer Service, and threatened to fire workers who did not follow the script. Although workers did not like being threatened, they complied, and the genre became more stable as it oriented toward a more specific goal.
  • At the micro level, think of genres as habits or reactions. These are operations that people have learned well enough that they no longer think about them. Examples include touch typing, double clicking, and shifting gears. The micro level is generally unconscious. When you see someone using a genre – say, reading a report – and you ask them what they’re doing, they’ll probably tell you, “I’m reading a report.” They probably won’t say: scanning the letters from left to right, then using my finger to turn the page.The micro level happens on the order of seconds. Finally, at the micro level, genres can encounter disruptions called “breakdowns.” Think in terms of habits that almost always work, but that misfire in a particular instance, causing the person to stop focusing on their goal and start focusing on their operations.
  • For instance, I tested GIS-ALAS with students who were taking a course on GISes. One thing that they spotted immediately was that in normal GIS maps, they only had to deal with a few layers of information, and those layers followed specific conventions drawn from traditional maps. For instance, water is always blue.But GIS-ALAS handles dozens of layers of information, and it assigns them colors at random. Rivers might show up as purple (here) or light green; accidents may show up as blue, the color of water. The students immediately protested that violating these conventions made the map much harder to interpret. Rather than focusing on the accident data, they focused on this violation of their expectations.
  • We can see a micro-level disruption in Network Chapter 1. In Case 4, local and long-distance provisioners are talking about a particular order, a “prem-to-prem” order, and they disagree with each other about what’s involved. Suddenly one of them realizes that “prem-to-prem” simply doesn’t mean the same to the two groups. When the two groups are using the same term to mean different things, they encounter a momentary interpretive disruption, a breakdown. Once they realize that the breakdown has occurred, they can repair it. Each group describes what they mean by “prem-to-prem,” and as they do, they learn more about each others’ (meso-level) goals and (macro-level) activities.In fact, this instance is a good example of how repairing a disruption at one level can result in changes at other levels.
  • When we look at both these dimensions – genre development across time and genre use at different levels of activity – we are engaged in “genre tracing.” We get a much fuller idea of how genres have developed, how they work in action, and why people have problems with them.
  • Developmentally, we get to see where genres originated, when they entered the activity, and how they changed the activity.In activity, we get to see how these genres concretely affect the activity at various levels.
  • Looking across both these dimensions helps us to make sense of disruptions. When students protest the colors in which a map is rendered, we can understandWhere the disruption occursHow it relates to disruptions at other levelsWhy the map violates their expectations
  • We can also detect interference across different activities. For instance, a GIS is set up to address an activity that is very different from that of the DOT. The representation systems have different logics and orientations because they address different activities.
  • So let’s apply this analysis to social media. In terms of development, we can look for origins: from what genres did the social media develop?For instance, LinkedIn is explicitly based on the resume genre and is oriented to the same sorts of problems that resumes solve. It also incorporates at least one related genre, the recommendation letter.Similarly Facebook was explicitly based on the face book genre and was originally oriented to a similar problem, associating faces with names.But we can also interview people to see how they use social media in their own lives. For instance, I’ve talked to freelancers who see LinkedIn as an information sheet for their business – they don’t want to get hired, they want to be contracted or subcontracted. Similarly, some Facebook users regard it as a social scrapbook. That is, they begin to interpret these social media from the vantage point of different traditions and reinterpret its features based on other genres with which they are familiar.
  • We can also consider social media use at different levels of activity. For instance, here’s something I tweeted a while back. On the level of activity, you might examine what my overall cyclical objective for using Twitter is. Possibilities might include self-promotion, enjoyment, and gossip. As it turns out, I tend to provide a running commentary on my bus trips as a way of figuring out how the bus works. It’s like a loose ethnography.But if you asked me at that moment what I was doing, I might say, “I’m tweeting this observation.” I see things that strike me as interesting or unusual and I report them.At the level of operation, I use unconscious habits. I type on my phone. I sometimes encounter breakdowns when I strike the keys incorrectly, producing a misspelled word that the phone then autocorrects incorrectly. I also use typing conventions that are simpler than those I would normally use.
  • As we’ve seen, genres don’t exist in isolation. In fact, they interact with each other in fairly complex ways. They are more than the sum of their parts: their interaction yields new capabilities.We can think of these as “ecologies” of genres, each genre addressing a set of issues and relating to other genres in various observable ways.
  • But these genres come from different activities and traditions, (with different assumptions, logics, notations, etc.), meaning more possible disruptions. We’ve already seen how difficult it is to bridge the connections among some of these. And when these difficulties mount, we often see unofficial workarounds, such as the sticky note that the police officer used to relate node maps and dialog boxes. When you begin seeing workarounds, you typically have some sort of discoordination between genres.
  • When you observe people using genres, you can see them relate those genres in various ways:(read)This isn’t an exhaustive list. And it can’t be done in an armchair. As you observe people using genres, you will likely find some surprising connections, and every connection gives you insight into how they solve problems and how their existing resources support or sabotage that problem-solving.
  • One way to map these relationships is through a genre ecology model. Notice that this is simply a network diagram. We’ll be discussing these more in Network Ch. 5, but let’s briefly discuss them here.The lines are not directional flows; they simply represent kinds of relationships among these genres. For instance, you can see that some people related node maps directly to dialog boxes; but others used intermediary genres such as handwritten notes or photocopies of the maps. Others performed oral readings – that is, they repeated the six-digit number until they typed it in. These innovations indicate that the two genres aren’t easy to connect and need mediation.One more thing. Notice how heterogeneous these genres are. Genres are often adopted from various other activities, opportunistically and haphazardly, and they don’t necessarily share the same logics or orientations. Genre ecologies usually aren’t planned. This is part of the reason why we start seeing tensions develop in activities.
  • Of course, we can’t simply rely on what individuals say or think about the connections others make in an organization. One great example is in Network Chapter 1. Anita, who works at the Internet Help Desk, is sick of handling issues that she thinks the salespeople should handle. So she starts writing angry, sarcastic notes in the logs. She tells me that she hopes upper management will begin to see these patterns and tell Sales to knock it off.But in reality, upper management doesn’t read these logs. There’s no actual connection between this genre and the set of genres that upper management uses for review and management.Who does read these logs? Anita’s coworkers. Anita is simply preaching to the choir.
  • Let’s apply this insight to an earlier example. In the case of LinkedIn, we might find that we can’t understand social media use by looking at the genre by itself. For instance, when I conducted a recent study on how freelancers work, the freelancers told me that they saw LinkedIn as part of a larger media strategy that included their business’ websites and other social media such as Facebook and Twitter, and connected with their use of email, conference calls, and the products of their work. That is, for them, LinkedIn was part of a larger ecology of genres that they had to deploy in order to carry out their activities. Without understanding these linkages, we don’t really understand the genre as a tool-in-use.
  • So we’ve just about reached the end of this slide deck. Here are some of the basic takeaways you should be able to apply to your own work.
  • And as you do, you should be able to apply them in specific ways.To help you apply these, I’ve developed some exercises that you can conduct by yourself or in teams.
  • Exercise 1 focuses on genre development.
  • Exercise 2 focuses on levels of activity. Select a genre that you’ve seen being used, then examine it at different levels.
  • Exercise 3 focuses on genre ecologies. I’ve outlined this as a thinking-and-discussion exercise, but you can also follow instructions in my book Topsight in order to model genre ecologies (there called “resource maps”).

Transcript

  • 1. Chapter 1. Networks, Genres, and Four Little Disruptions Clay Spinuzzi Clay.spinuzzi@mail.utexas.edu How to improve information flow in 1 organizations (c) 2011 Clay Spinuzzi
  • 2. Value• Understand concept of genre• Examine genre developmentally• Examine genre at different levels of activity• Examine how genres interact in an activity• Learn to map relationships among genres How to improve information flow in 2 organizations (c) 2011 Clay Spinuzzi
  • 3. WHAT IS GENRE? How to improve information flow in 3 organizations (c) 2011 Clay Spinuzzi
  • 4. Spinuzzi 2008, p.17• “not just text types”• “typified rhetorical responses to recurring social situations”• “tools-in-use”• “a behavioral descriptor rather than a formal one”• Through their use, genres “weave together” networks How to improve information flow in 4 organizations (c) 2011 Clay Spinuzzi
  • 5. …rhetoric?How to improve information flow in 5organizations (c) 2011 Clay Spinuzzi
  • 6. “typified rhetorical responses to recurring social situations” How to improve information flow in 6 organizations (c) 2011 Clay Spinuzzi
  • 7. NOT just structure How to improve information flow in 7 organizations (c) 2011 Clay Spinuzzi
  • 8. NOT Medium How to improve information flow in 8 organizations (c) 2011 Clay Spinuzzi
  • 9. A “Tool-In-Use” How to improve information flow in 9 organizations (c) 2011 Clay Spinuzzi
  • 10. DevelopmentLevels of activityTWO WAYS TO EXAMINE GENRE How to improve information flow in 10 organizations (c) 2011 Clay Spinuzzi
  • 11. “Genre (from French, genre, "kind" or "sort", from Latin: genus (stemgener-), Greek: genos, γένος)” – WikipediaSame root word as gene, genealogy, Genesis.GENRE DEVELOPMENT How to improve information flow in 11 organizations (c) 2011 Clay Spinuzzi
  • 12. Case: Accident Location and Analysis System (ALAS)• Based on Spinuzzi (2003), Tracing Genres through Organizations. How to improve information flow in 12 organizations (c) 2011 Clay Spinuzzi
  • 13. ALAS Evolved…• Before 1974• 1974• 1989• 1996 How to improve information flow in 13 organizations (c) 2011 Clay Spinuzzi
  • 14. Before 1974 How to improve information flow in 14 organizations (c) 2011 Clay Spinuzzi
  • 15. 1974 – Mainframe-ALAS How to improve information flow in 15 organizations (c) 2011 Clay Spinuzzi
  • 16. 1974 – Mainframe-ALAS How to improve information flow in 16 organizations (c) 2011 Clay Spinuzzi
  • 17. 1989: PC-ALAS How to improve information flow in 17 organizations (c) 2011 Clay Spinuzzi
  • 18. 1989: PC-ALAS How to improve information flow in 18 organizations (c) 2011 Clay Spinuzzi
  • 19. 1996: GIS-ALAS How to improve information flow in 19 organizations (c) 2011 Clay Spinuzzi
  • 20. 1996: GIS-ALAS How to improve information flow in 20 organizations (c) 2011 Clay Spinuzzi
  • 21. Accidents in the Cornfield How to improve information flow in 21 organizations (c) 2011 Clay Spinuzzi
  • 22. Genres Developed Over Iterations How to improve information flow in 22 organizations (c) 2011 Clay Spinuzzi
  • 23. Case: LinkedIn How to improve information flow in 23 organizations (c) 2011 Clay Spinuzzi
  • 24. Case: Facebook How to improve information flow in 24 organizations (c) 2011 Clay Spinuzzi
  • 25. Genre functions at three different levels.LEVELS OF ACTIVITY How to improve information flow in 25 organizations (c) 2011 Clay Spinuzzi
  • 26. Genre at three levelsLevel Focus Chars Timescale Aware? DisruptionMacro Activity Culture, Year, No Contradiction history; decades social action, social memoryMeso Goal Tool-in-use; Minutes, Yes Discoordination tactics hoursMicro Operation Rules, habits Seconds No Breakdown How to improve information flow in 26 organizations (c) 2011 Clay Spinuzzi
  • 27. Genre: The Macro LevelLevel Focus Chars Timescale Aware? DisruptionMacro Activity Culture, Year, No Contradiction history; decades social action, social memoryMeso Goal Tool-in-use; Minutes, Yes Discoordination tactics hoursMicro Operation Rules, habits Seconds No Breakdown How to improve information flow in 27 organizations (c) 2011 Clay Spinuzzi
  • 28. Genre: The Macro Level How to improve information flow in 28 organizations (c) 2011 Clay Spinuzzi
  • 29. Case 2: “Darrel thinks Gil is being Unreasonable”• Different genres focus on different aspects and make different assumptions. How to improve information flow in 29 organizations (c) 2011 Clay Spinuzzi
  • 30. Genre: The Meso LevelLevel Focus Chars Timescale Aware? DisruptionMacro Activity Culture, Year, No Contradiction history; decades social action, social memoryMeso Goal Tool-in-use; Minutes, Yes Discoordination tactics hoursMicro Operation Rules, habits Seconds No Breakdown How to improve information flow in 30 organizations (c) 2011 Clay Spinuzzi
  • 31. Genre: The Meso Level How to improve information flow in 31 organizations (c) 2011 Clay Spinuzzi
  • 32. Case 3: “Abraham Threatens to Fire Workers”• A freeform genre becomes more structured and oriented to a specific goal. How to improve information flow in 32 organizations (c) 2011 Clay Spinuzzi
  • 33. Genre: The Micro LevelLevel Focus Chars Timescale Aware? DisruptionMacro Activity Culture, Year, No Contradiction history; decades social action, social memoryMeso Goal Tool-in-use; Minutes, Yes Discoordination tactics hoursMicro Operation Rules, habits Seconds No Breakdown How to improve information flow in 33 organizations (c) 2011 Clay Spinuzzi
  • 34. Genre: The Micro Level How to improve information flow in 34 organizations (c) 2011 Clay Spinuzzi
  • 35. Case 4: “Jeannie Talks Past Local Provisioners”• The two groups of provisioners encounter breakdowns over the common term “prem-to- prem,” which means different things to them.• As they repair the breakdown, they realize differences at other levels. How to improve information flow in 35 organizations (c) 2011 Clay Spinuzzi
  • 36. Genre tracing DevelopmentLevels How to improve information flow in 36 organizations (c) 2011 Clay Spinuzzi
  • 37. Genre Tracing• Genre tracing involves examining genres across these two dimensions:• Development. How did a genre develop? What assumptions are bundled into it, from the designer’s side and from the user’s?• Levels of activity. How is the genre used at each level? What disruptions occur, and how are they manifested at each level? How to improve information flow in 37 organizations (c) 2011 Clay Spinuzzi
  • 38. Genre Tracing How to improve information flow in 38 organizations (c) 2011 Clay Spinuzzi
  • 39. Genre Tracing How to improve information flow in 39 organizations (c) 2011 Clay Spinuzzi
  • 40. Genre & Social Media: Development How to improve information flow in 40 organizations (c) 2011 Clay Spinuzzi
  • 41. Genre & Social Media: Levels of Activity How to improve information flow in 41 organizations (c) 2011 Clay Spinuzzi
  • 42. Examining how genres relate to each otherGENRE ECOLOGIES How to improve information flow in 42 organizations (c) 2011 Clay Spinuzzi
  • 43. Genre Ecologies How to improve information flow in 43 organizations (c) 2011 Clay Spinuzzi
  • 44. Kinds of relationships among genres• juxtaposition (two texts attached to or overlapping each other)• placing (two texts placed side by side, in a stack, or in regular places)• annotation (writing or altering a text)• transfer (using one text as source for filling in another)• modeling (using one text as a model for another)• reference (using one text to interpret or operate another)• And ??? How to improve information flow in 44 organizations (c) 2011 Clay Spinuzzi
  • 45. Genre Ecology Models How to improve information flow in 45 organizations (c) 2011 Clay Spinuzzi
  • 46. Case 1: “Anita Thinks Geraldine is Slacking”• Participants sometimes think there are connections when there aren’t. How to improve information flow in 46 organizations (c) 2011 Clay Spinuzzi
  • 47. Genre Ecologies & Social Media How to improve information flow in 47 organizations (c) 2011 Clay Spinuzzi
  • 48. Takeaways• Genre as social action• Genre development• Genre at different levels of activity• Genre ecologies How to improve information flow in 48 organizations (c) 2011 Clay Spinuzzi
  • 49. Applications• Determining what genres are being used.• Determining what they’re for.• Determining where they’re from.• Examining how they’re used at different levels.• Examining how they connect. How to improve information flow in 49 organizations (c) 2011 Clay Spinuzzi
  • 50. Exercise: Genre Development (5 minutes)• Identify genres that have developed in your project, especially in social media: LinkedIn, Facebook, Twitter, etc.• Select one. How has it developed over time? Can you identify preexisting genres from which it developed?• How is it perceived by users? Do they associate it with other familiar genres? How to improve information flow in 50 organizations (c) 2011 Clay Spinuzzi
  • 51. Exercise: Levels of Activity (5 minutes)• Select a genre from a work example.• Macro: Where did this genre come from and to what sort of problem was it originally oriented? Think in terms of originating activities (ex: LinkedIn profile: based on job search)• Meso: What conscious goals might users identify as they use it?• Micro: What sorts of unconscious habits of interpretation and use are involved in using it? How to improve information flow in 51 organizations (c) 2011 Clay Spinuzzi
  • 52. Exercise: Genre Ecologies (5 minutes)• Identify sets of genres in a work example, especially unofficial, improvised innovations.• How are they related in use? Think in terms of relationships such as juxtaposition, placing, annotation, transfer, modeling, and reference.• If you must speculate, that’s okay – but observed relationships are better. How to improve information flow in 52 organizations (c) 2011 Clay Spinuzzi