(NB: This post is about politics and rhetoric, not social computing or education.)
I understand that parts of the movie Game Change paint a sympathetic portrait of Sarah Palin, but the clip they showed at my college reunion made her look like a fool. The author of the book the movie is based on, classmate Mark Halperin, was interviewed on stage at our class talent show, and the result was hilarious (though smug). Mark polled the audience: how many people think Sarah Palin is qualified to be president? Three lonely, brave souls raised their hands. One of those souls, Robert Miller, then walked out, disgusted. Robert later posted to our class Facebook group that he felt “mugged” by the experience.
I walked away from this experience wondering if some of my fellow liberals understand why a political candidate like Sarah Palin appeals to so many people. I have a conjecture about the nature of the gap. Rhetoricians talk about logos (appeal to reason), ethos (appeal to credibility), pathos (appeal to emotion), and kairos (timeliness). In the lively (and gracious) Facebook debate that followed this incident, talent show host Peter Sagal asked if this all had something to do with the role of pathos in politics. I think he’s on the right track, but the key is actually the distinction between logos and ethos. Liberals tend to privilege reason, and conservatives tend to privilege credibility.
My dental hygienist once told me, “I just love Sarah Palin! She’s so real!” On election day in November 2004, one of my PhD students stayed home all day to agonize over who to vote for. We were in the middle of a tight deadline, but he took a whole day to brood over it. I asked him what he was thinking, and he said “Well, George Bush feels like a regular guy. Someone you could have a beer with.” He ultimately voted for John Kerry, but it was painful for him to pull the lever for someone he saw as a smug, condescending elitist. These are just anecdotes, not data, but they certainly got me thinking.
When you think about it, privileging ethos isn’t crazy at all. No one really knows what they’re doing when you come down to it, do they? Unanticipated consequences run amok in the wake of the most carefully researched plan. So isn’t what kind of a person you are the important thing? And isn’t it fair to say that folks who smugly think they’re better than everyone else may not be in touch with what’s important to real people? I’m an unrepentant logos person myself–I want someone with a logical answer for why they’re qualified for example to lead US foreign policy. But I think I am beginning to understand the other view, and would like to come to understand it better.
CSCW research is exploding in popularity, and with good reason. It’s hard to think of an area of life that is untouched by collaborative uses of the Internet. But as I’ve been watching the growing body of research over the last few years, I’ve noticed some methodological challenges that pop up repeatedly. I want to highlight three problematic patterns:
1. Working with the Data We Have
So you start off with a set of research questions that meet some heuristic for Interestingness, and pick a real, deployed system for which those questions are relevant. Then you collect the data you can access, with available staffing and with limits on data access. The data you can get without herculean effort doesn’t quite answer the questions you started with. So you weave a story around the data you have. Which ends up lamely dancing around its inability to answer the Interesting questions, or addresses different convenience questions that aren’t especially interesting.
2. Over-Generalizing from a Toy System
To avoid Pitfall 1, you decide to design an entire system so you can control every aspect of it and collect richer data. You make a simple system so you’re not spending years developing, and anyway the point is in the user data–you’re not trying to make a deployable system. You pay or cajole people into trying out the system in a lab experiment or short-term deployment mode. Then you write up the results and attempt to generalize to problems of broader interest. But does what people do with a toy system really speak to what they do in more realistic situations? Probably not, but you make sweeping claims anyway.
3. Build a Giant System and Run out of Time and Resources
OK, so to avoid the toy system pitfall, you decide to build a real system. Something that real people will want to use. You spend two years doing software development. Then you spend another year in iterative design with initial testers. Then your money is spent and your students have graduated and… I know there was a reason we built this monster, but does anyone remember why? The study that the system was built to facilitate never gets properly done because we’re out of resources and all of this took so long that the world has moved on anyway.
These are pessimistic scenarios, but I’ve personally fallen into all three pits to one degree or another. Are these familiar to you too? I’d like to start a community discussion about what we as a field can do to avoid them. My first hunch is that maybe we need larger scale projects. All of these are to some degree side effects of limited resources. Maybe one or two PhD students working with one faculty member isn’t the right size intervention to accomplish real work in this field. That’s one idea. What else?
I confess that I love a good heartwarming meme about folks helping strangers. I saw one yesterday about strangers sending letters to a dying man with Down’s Syndrome who loves to get letters (I can’t find the link again and don’t know if it’s true.) And who didn’t enjoy watching a hundred people come to the cardboard arcade made by a bored kid over his summer break in LA? The subtext of these stories is that people are nice. Look what they’ll do for a total stranger!
Clifford Geertz wrote that culture is a story we tell ourselves about ourselves. And each time I’m touched by a new tale of kindness, a cranky little voice in my head says: It’s a lie! We’re telling ourselves stories about how much we care when honestly we don’t. Yup, folks did help that one clever, cute kid. For one day. But what about the rest of his days? And what about the other cute kids of working parents who have no affordable summer programs to attend? What about the ones who can’t hang out at a parent’s workplace? What about the ones who aren’t especially clever or cute? I guess they’ll get nothing unless an underemployed film maker takes an interest in them.
Social media is pretty good at mobilizing large numbers of people for heartwarming gestures. But helping one person with a compelling story is just a gesture, and a trivial one. I wonder how we can use the same medium to push for more meaningful change–like voting for investment in social programs. Large scale programs that can attempt to help everyone in need–whether or not a film maker happens by.
“The system is fully implemented, but we can’t afford to make it public yet because of the hosting costs,” said Brent. I was asking Brent Hecht about his work on Ominpedia (my personal favorite project at the CHI conference this year). Brent is working on using caching to lower the hosting costs and hopes to make the system public, but his situation got me thinking. What are the advantages and disadvantages of hosting research projects on the cloud?
The advantages are clear: Eric Gilbert tells the story of his Link Different project unexpectedly going viral. His university server couldn’t handle the load. A cloud hosting setup would have handled this seamlessly. But then again, could his university budget have handled the bill?
My MediaMOO system was launched in 1993, and MOOSE Crossing in 1995. Believe it or not, both are still running. At one point I was going to close MediaMOO, but member Michael Day said he didn’t want to see it go away, and took over the server. When I moved from MIT to Georgia Tech in 1997, I bought a UNIX box to run MOOSE. Since then I believe it had to be moved to a new machine once–to a machine someone else no longer wanted that my IT department let me have for free. We bought the box, and we can just leave the server running on it. I reboot it once or twice a year (current MOOSE uptime: 5 months+). Just last week an ed tech researcher asked me some questions about MOOSE and I set him up with an account. He had read a couple papers and now he can actually try it out.
There is a value in keeping significant old systems around, even if they no longer have active user bases. A cloud hosting model seems so right to me–it’s scalable and robust. It just makes sense. But the hosting costs are a problem. Even if the total amount of money is small, grants are for specific work and have end dates. I can still be running a 10+ year old UNIX box, but I can’t still be paying hosting fees for a research project whose funding ended years ago, no matter how small that bill is. Grants end–there’s no provision for “long term hosting.” Our library can help us archive data, but they are not yet ready to “archive” an interactive system. I hope companies that provide hosting services will consider donating long-term hosting for research.
Developing research online systems, we typically work on short-term research grants. Those grants are time limited. It’s easy for me to put money for a server in a grant, and leave it running. The hidden cost is the burden on my university IT department, but little servers don’t cause a lot of trouble.