First off, falsifiability is only one criterion for a science. Not all philosopher’s of science agree that it’s as central as Popper(?) claimed. It’s not obviously useful when talking about social science, where there is not an objective background against which a theory can be complared. The research goals of learning object researchers and semantic web researchers rarely involve understanding nature. They have no external objective standards to compare themselves to; at best, there are internal consistency and coherence standard.
Social scientists measure things like the opinions, beliefs, reactions, etc. of *people*. The semantic web and learning objects are both apparently interested in satisfying various people (i.e. “web users” and “learners”), and so the only sensible measure of its overall success is a measure of how satisfied these people are. Thus they are clearly in the domain of social sciences. You are not judged by how well you match up with nature; you are judged by your users.
Practical measures of success include uptake by other scientists (especially in different fields), or use by companies. It strikes me as close in spirit to economics.
Of course, there is a wide range of activity in Computer Science, so it’s probably not possible to say something like, “Computer Science is X,” where X is one thing. Having said that, I wold say that a good part (the most science-like part) of Computer Science is math. CS by and large doesn’t seek to understand the natural world. If we seek to understand anything, it is the properties of formal systems: computers. Some folks just like to play with formalisms. To the extent our work isn’t in the form of formal proofs, its just that we lack the tools to do such proofs.
The other major part of CS is engineering, which of course is applied science. But again, we’re mostly not trying to understand the natural world, but rather to use our knowledge to build devices (in the broadest sense, including software as a device).
Having said that, there are those few people who actually do science in CS. Some of the Human Factors people try to understand human perception and cognition in the context of interaction with computers (or not in the contex, even).
I don’t think this is a matter of hard versus soft science. To me, the distinction there is not the goals but rather the lack of deep theory for the latter. Most of CS has different goals.
First off, falsifiability is only one criterion for a science. Not all philosopher’s of science agree that it’s as central as Popper(?) claimed. It’s not obviously useful when talking about social science, where there is not an objective background against which a theory can be complared. The research goals of learning object researchers and semantic web researchers rarely involve understanding nature. They have no external objective standards to compare themselves to; at best, there are internal consistency and coherence standard.
Social scientists measure things like the opinions, beliefs, reactions, etc. of *people*. The semantic web and learning objects are both apparently interested in satisfying various people (i.e. “web users” and “learners”), and so the only sensible measure of its overall success is a measure of how satisfied these people are. Thus they are clearly in the domain of social sciences. You are not judged by how well you match up with nature; you are judged by your users.
Practical measures of success include uptake by other scientists (especially in different fields), or use by companies. It strikes me as close in spirit to economics.
Of course, there is a wide range of activity in Computer Science, so it’s probably not possible to say something like, “Computer Science is X,” where X is one thing. Having said that, I wold say that a good part (the most science-like part) of Computer Science is math. CS by and large doesn’t seek to understand the natural world. If we seek to understand anything, it is the properties of formal systems: computers. Some folks just like to play with formalisms. To the extent our work isn’t in the form of formal proofs, its just that we lack the tools to do such proofs.
The other major part of CS is engineering, which of course is applied science. But again, we’re mostly not trying to understand the natural world, but rather to use our knowledge to build devices (in the broadest sense, including software as a device).
Having said that, there are those few people who actually do science in CS. Some of the Human Factors people try to understand human perception and cognition in the context of interaction with computers (or not in the contex, even).
I don’t think this is a matter of hard versus soft science. To me, the distinction there is not the goals but rather the lack of deep theory for the latter. Most of CS has different goals.