Beki Grinter

Posts Tagged ‘devolution of computing’

Computer Science: Why I care

In academia, computer science, discipline, HCI, research on October 14, 2009 at 6:23 am

As I’ve said before, I’m very interested in disciplinary evolution. There are many reasons, but one of them is that I’ve been discussed as an example of someone who is not a Computer Scientist. At least three things bother me about this discussion. First, these criticisms are largely said about me and not to me. Second, it assumes that the discipline of Computer Science can be defined, and I don’t think evidence supports that. While I don’t completely agree with Eden’s arguments (as an example of writing about multi-paradigmatic behaviour in CS), I do concur that we’re proceeding in multiple distinct paradigms that come with different, possibly irreconcilable methodological, ontological, and epistemological assumptions, which makes me wonder whether we do collectively know what the discipline of Computer Science is all about. Third, the criticism also dismisses the commitments I’ve made to my profession as well as the assessments I have had by others regarding the role of my research in the field of Computer Science (an obvious example, I publish in conferences that are mostly sponsored by the ACM, the professional association for Computer Science researchers, and others cite my work in other Computer Science conferences).

I have three degrees, all in Computer Science. While degrees do not make a Computer Scientist, I would suggest that they give me many years of training for understanding what is included in Computer Science. But degrees can not define a Computer Scientist. After all some of most significant innovations come from people who don’t have degrees in Computer Science. No-one is what their degrees say they are, it’s what they choose to do and why.

So, my commitment to Computer Science was cemented in graduate school. I went to graduate school at UC Irvine. The other day I found a paper that discussed the program I was in in graduate school (the Computers, ORganizations, Policy and Society (CORPS) group). It was not HCI, although it was similar, it was focused on Computing as an empirical science, combining a priori theories that can explain technologies in use-context, with a posteriori empirical analysis of what happened when technologies were deployed in particular contexts. I was hooked, this made the Computer Science of numerical analysis, formal methods, graphics, make sense to me.

Three and a half years later I graduated with an MS and PhD. My thesis work explained how dependencies in code reflected dependencies in the division of labor, and showed how these labor relationships were not being accounted for in the processes used to develop software. Because of this, I received an offer of employment at Bell Labs, and I joined the Computer Science research division of Bell Labs. My job description, continue to do Computer Science research on the human-centered problems that continue to plague software development (in 1960’s it was a crisis, in the 1990’s it became a chronic crisis, and apparently hell). I’ve written about how amazing this time was, how much I learnt. Bell Labs demanded excellence in science, it was a world-class research laboratory, and so it held us all to the highest standards of research in our discipline: Computer Science. So, each year I continued to do research in this space and had the honour (it was terrifying at times) to have my performance assessed by the type of people whose contributions to Computer Science are central to the discipline. But, of course this was simultaneously the privilege of working at Bell Labs, to have your own standards set by people who made Computer Science.

Four years later it was clear that Bell Labs was going to go through what many nationally acclaimed scientific laboratories go through: downsizing. I joined the Computer Science Laboratory at Xerox PARC, as a member of the Distributed Systems area (why this comes as a surprise to people I do not know). CSL was very similar to Bell Labs, but PARC is physically smaller than Bell Labs was. So, that made it more intense, the evidence of PARCs contributions to Computer Science were everywhere, you could physically see them (like the Ethernet). Again, what I was responsible for doing was to advance Computer Science, that’s how I was judged.

So, my entire career through Bell Labs and Xerox PARC was as a practitioner of the research of Computer Science. That’s who mentored me, set the standards, and evaluated my contributions, with the help of external communities of researchers who accepted my papers into journals and conferences in the discipline of Computer Science.

From there I joined Georgia Tech, and one day I discovered  that I was in the School of Interactive Computing. And I like it very much. But, I think there’s some confusion about whether Interactive Computing is Computer Science. To me the answer is obvious, it’s the third paradigm of Computer Science. Its an empirical experimental discipline, drawing on a priori theory to inform computer program design, some of which are programs designed to push new computational space (such as robotics), others of which are designed to probe phenomena (like learning and how people do so). We use empirical scientific investigation to determine whether we have been successful, and if we have not what has failed. It is the science of computing that is the raison d’etre for Interactive Computing.

To those who have told someone, but not me, that I don’t do Computer Science this is my response. Computer Science is complicated to define, and we’d all be better served understanding it more deeply. And I am lucky to have had a career where the standards of engagement and assessment were set by people whose contributions to Computer Science are clear: who have collectively done the important work of defining the field. And I will also note here that I never heard any of those people discussing who was not Computer Science, they were far to busy trying to actually develop the field. Finally, I want to close with the comment that I am categorized as a minority in Computer Science because I am a woman. I struggle with that categorization, but I believe that some of the choices I made professionally have come with higher costs for me than they would if I had been a man. So, one reason I am very committed to Computer Science is that I’ve given a lot to it, but it came with costs—things I reluctantly gave up to pursue a career in Computer Science.

Lucky Physicists and the Devolution of Computer Science

In academia, computer science, discipline on May 26, 2009 at 9:43 pm

There are two types of books one can read about academia.  One genre is the academic “how to” book.  I’ve just written about an example in that genre that I found particularly insightful.  The second is a genre that studies academia itself.  It’s in this second genre that I conclude that Physicists are lucky people indeed.

Many years ago I read Sharon Traweek’s Beam Times and Life Times.  It certainly explained the person I was dating—a High Energy Particle Physicist. But, to the book. The book was a multi-sited ethnography of the way that Physicists work. The division of work differs from computing, and those differences emerge at the graduate school level. Working in teams is the simplest way to describe it, hierarchical teams composed of theorists and experimentalists. Traweek’s book did a marvelous job of explaining how these teams produce the science of physics.

And yesterday I started reading another book about Physicists. This is a longitudinal ethnography, following 55 Physicists over some years, understanding how their career evolves over time and as they climb through the ranks of Assistant, Associate and finally Full Professor. I’ve only just started it, but already I’m finding it quite instructive in thinking how an academic career evolves, and what that might me for me.  (It also makes me think that I should assign the two books as an example of how ethnography is not just one thing, but a methodology that consists of numerous approaches to data collection and analysis. If you add in Latour and Woolgar’s Laboratory Life, you have a third approach to studying scientists and the doing of science).

Computer Scientists by contrast have been woefully neglected in study. There are some books about engineering in practice (Kunda, Bucciarelli for example). But not in the academic context. And this is a shame, because Computer Science is in an interesting transitional period, and one with particular implications for the organisation of the academy. (It almost certainly has other implications but lets begin here for now).

Computer Science is in a period of devolution. Devolution into entities that will be the intellectual home for an increasingly divergent number of research problems and pursuits that go under the banner of Computer Science. One can view this devolution as entirely pragmatic. The field is still fairly young and has enjoyed explosive growth and rapid change (both can be attributed to not just the increased presence of computing technologies, but also the radical transformations that they’ve undergone in terms of size, power, form factor, that fuels a series of alternate possibilities, there are certainly other reasons, these just strike me as some obvious ones). In response to this explosive growth and rapid change, so Computer Science has expanded to accommodate the new research problems and domains made possible. And, here’s the really pragmatic part, the organization of Computer Science as a discipline is now stretched, well it seems to be. Perhaps it’s because no-one wants to have faculty meetings with 100 people that you see an increasing devolution of previously single, monolithic Computer Science entities (such as the College of Computing at Georgia Tech, or the Information and Computer Sciences School at Irvine into a series of smaller organizational entities). (There’s more to say about Computer Science organized inside Engineering and the rise of the ISchool movement, and perhaps I will at some future date).

These smaller entities interest me greatly. Again comparing GT and UCI, we see three different sub structures: Computer Science, Informatics, and Statistics at Irvine, while Georgia Tech has Computational Sciences and Engineering, Computer Science, and Interactive Computing. Do the names matter. Yes, I think so. For many other departments these names are the names of their disciplines (such as finding Physics, Chemistry, Civil Engineering departments that characterise what goes on in them and with what disciplinary entity that research work is associated).

So, lets take a case of a particular speciality in Computer Science, Software Engineering. At GT you’ll find it in Computer Science, but at Irvine it shows up in Informatics. What implications does that have for the practice of Software Engineering? Right now, I don’t know. But will it, that’s what I wonder. When will what we have become institutionally become what we do, our community of practice professionally.  Then there’s a question of contested turf. The name Computer Science is not without problems, particularly when applied to one piece of the organization. Is the implication that what is not within Computer Science is not Computer Science research?  If my boss read my blog, I think he’d groan about this particular comment, one way I have experienced this is rather personally (yes, I certainly do research that’s unusual in Computer Science, I thought that that’s what taking risk meant, but I have always taken the risk identifying myself and my purpose as being invested in the future of Computer Science research).

But there’s far more on the table here than my own ego (big as that is). I think what might be on the table is institutional legitimacy and disciplinary organization and discourse. If an organization is structured to make something possible, it simultaneously hinders other things. What is organizationally near (i.e., in the same organization, sharing a common reporting structure, the more the better) is far easier to accomplish than what requires crossing organizational divides. So, we can view the organization of Computer Science as a set of bets, bets about what will happen if we put some things in the same place and separate others.  I wonder how to place those bets, but that’s because I find organizations absolutely fascinating. At the same time there’s an undercurrent—a suggestion—of a separation that might result in a smaller discipline of Computer Science, and something larger that encompasses the full span of activities that formerly used to be Computer Science (and a question of what that larger entity is, what it’s project is, what space does it occupy at the national level). Transition can be difficult, graduate school is a preparation for a career of identity management and assessment (my vita speaks volumes about Computer Science research, and says nothing about Physics research, not just in content, but also in the mechanisms used to produce that content like whether I choose to write books, journals or conference papers). The Institutional legitimacy I think turns on this somewhat, that identity is not just what we do, but what is given back to us by our ability to affiliate with a particular research discipline.

So, I wish someone had studied us. I wish I could pick up a multi-sited ethnography, preferably also longitudinal, to get some ideas about what if anything my colleagues are doing about the devolution of Computer Science. How they handle identity and legitimacy. What organizations are being created and how that gives rise to new areas of research, and what it might potentially close off. Lucky Physicists as their field evolves they have a series of guides to stimulate and frame questions. But, perhaps we are lucky enough even despite not having these studies because what we have an abundance of right now is opportunity. Opportunity to rethink it all, if we are quick, thoughtful, and open to devolution.