A typical career for a tenure-track professor might look like this: Publish early and often, hit a peak after a few years, get tenure, then watch productivity decline until retirement.
Given this popular perception of the scholarly arc, professors of all stripes have been evaluated at a young age by the number of published papers they produce. But a study from the University of Colorado at Boulder suggests that this assumed trajectory is mistaken.
Samuel Way, a postdoctoral researcher in Boulder’s department of computer science, and colleagues there examined the paths of more than 2,400 tenure-track faculty members in 205 computer-science departments in the United States and Canada, using more than 200,000 publications as data points.
While the conventional narrative of the “rapid-rise, gradual decline” curve of productivity held true on average, a closer look into the individual trajectories of researchers told a different story. About 20 percent of researchers conformed to the average path, the researchers found, but the other 80 percent had careers of more varied productivity.
The Chronicle asked Mr. Way to explain why the results are important for fellow researchers. An edited and condensed transcript of that interview follows.
Q. What led you to ask this research question?
A. There’s about 60 years of research of scientists studying each other’s productivity, and basically all of them find this same trend where you have that same shape … that rapid ramping up of productivity and then the gradual decline. This has been observed for mathematicians, for psychologists, for biochemists, you name it, and in other studies of productivity as well. So the production of art, of crime, of points in basketball — there’s many, many studies that find the same curve.
But when we sat down to write our own model for how to capture these things, we noticed that these trends and these models that recapitulate or reproduce them are describing averages or aggregate trends of individuals. In some cases hundreds or even thousands of researchers were taking their productivity and averaging it together over time. And when we did this for computer science we got a very familiar looking curve. But really the motivating question was to understand whether the average of all researcher productivity trends is a fitting description or yardstick for what happens for individuals.
So does the average of all researchers resemble the average researcher? If I go to the computer-science department down the hall and I grab one of the faculty there and I look at how productivity was distributed over the course of their career, does it follow the same sort of trajectory? That’s really what this whole study is about. We wrote down a really simple model for how to describe the shape of a person’s career, and then we applied that model to basically all-tenure track faculty in the field of computer science and noted how many of them resembled this rapid-rise, gradual-decline narrative that’s so pervasive in the literature.
Q. What was the most surprising finding?
A. Definitely how few people fit the mold. So this is a pattern that’s been observed for about 60 years, and in a lot of ways it’s entered as an expectation for what faculty ought to look like in terms of their careers. To apply a simple model that determines that only about a fifth of researchers actually have careers that actually resemble that expected curve was definitely a big surprise.
Q. Could this also be true in other disciplines?
A. We definitely expect that our results hold for other fields. If we take our data and we average it in the same way that all of these other studies have done from psychology to biochemistry, we see the same curve in computer science, and we find that same signature when looking at the average of everyone.
The fact that we see the same aggregate pattern for computer science that we found over and over again, we don’t have any reason to believe that this wouldn’t apply to other fields as well.
Q. What do you hope this research changes?
A. The part where we’re describing sort of the overall shape of a person’s career, that’s only looking at people that have long careers, careers between 10 and 25 years, those folks that have tenure. These were people who stood in front of a panel of their peers and were deemed successful in one way or another.
What this tells us and what we hope people will take from this is that there are a lot of ways to have a successful career in science, and only some of them correspond to having a productivity trajectory that looks like the conventional narrative describes.
For hiring committees and tenure committees, this basically serves as a cautionary tale against using just productivity as a yardstick for determining the quality of the person’s career, that there are many other ways that a person can find success. And that’s not necessarily reflected in the number of publications on their CV.
Q. What’s the most important thing for professors to know?
A. I’ve definitely had people tell me that they take some comfort in knowing that there are other people that have careers that look like theirs, that they want to know where they fall on this map.
It’s not that when you’re in the later stages of your career you’re predestined to be an unproductive scientist. Productivity comes and goes.
I’ve shown these figures at computer-science conferences and people want to know, like, Where am I in that plot? And it’s comforting to know that not everyone looks the exact same way but that all of these people have found success and have built a career for themselves in science. For the average person, I think that’s a key takeaway.
But also to take some encouragement in knowing that productivity can come and go at different points in your career, it’s not that when you’re in the later stages of your career you’re predestined to be an unproductive scientist. Productivity comes and goes.
Q. What’s your experience in this field?
A. We wrote another paper a year and a half ago that looked at the role that gender plays in faculty-hiring networks and whether or not men and women are treated differently by the faculty-hiring process. “Science of science” and biology are both fields that have sort of recently come in to mountains of data, and there’s a lot of really incredible, fascinating work to be done to make sense of it. They’re both fields where there really are no rules for how to analyze things.
These problems are new enough that there are not textbooks written about the exact solution to how you go about these things. It ends up being a lot of fun for computational folks to enter into these pictures and bring along with them these tools and techniques from math and engineering. It’s a fun playground.