When I started my PI career, my first cover letter to a glamour journal emphatically pointed out that my cutting-edge, ground-breaking work was the first and firstest to do X.
Feedback from senior colleagues was: “Drop that blech! Better say what your insight into X actually is, and in what way it is profound.” — Good advice. Because novelty is overrated, insight rules.
How should novelty be valued in science? Not exclusively.
So I wasn’t too surprised how Barak Cohen answered the question “How should novelty be valued in science?” in the last issue of eLife. I would never put a question mark into a title, if the answer is so clear:
I conclude that placing too much value on novelty could have counterproductive effects on both the rate of progress in science and the organization of the scientific community.*
To reach this conclusion, Cohen uses tools many of us scientists do not have in our toolboxes: philosophy and sociology! Popper, Kuhn, Lakatos, Merton and a whole lot of thinkers I had never heard of. This is quite a tour de force of the theory of science.
Very well done! And if you don’t believe me, look at what editor Peter Rodgers, and reviewers Yitzhak Pilpel and Angela H DePace had to say in their decision letter:
“The paper is an impressive scholarly work. It is broad, deep and methodological”.
(As an aside, I think it is really cool eLife makes decisions so transparent.)
And all of it leads to one conclusion/proposition:
If we want to solve important practical problems then progressive research programs that expand and refine the predictive power of existing models are at least as important as research programs focused on novel hypotheses. One suggestion would be to replace the current emphasis on novelty with an emphasis on predictive power, particularly quantitative predictions.*
Nothing wrong with being quantitative, but …
Well, I wouldn’t be writing this, if I hadn’t something to grumble about. And it is a point the reviewers had already spotted:
The solution presented at the end (to focus on quantitative prediction as a gauge of novelty) is only one of many possible solutions, and it would be good if the author could discuss other possible solutions, although we should not insist on this.
I would argue that another solution would be including some description of the sociology of science in graduate and undergraduate education, such that the value of novelty and reproducibility/extension at the community level are more clear to people.
Right now we almost exclusively lift up isolated geniuses as scientific heroes; is it no wonder that everyone chases some paradigm shift of their own? I’m sure there are other solutions as well.*
Cohen points out that many, if not most, molecular biology models are qualitative cartoons, rather than quantiative and predictive models. This is correct, and making them more quantitative is definitely a worthy goal (says the organiser of a Quantitative Biology seminar series).
But it is not clear to me, why quantitation is the cure for our obsession with novelty.
Like the reviewers I think there must be other solutions, many of which should start in education. Natural science curricula teach very little history, sociology or philosphy. No wonder that scientists find it hard to take a step back and reflect on the bigger picture of the endeavour they are engaged in. But changing curricula is neither easy nor fast. Getting computation into biology eduction is hard enough, getting philosophy into it as well might be completely infeasible.
As a more short-term solution, I would argue that we (and that means you, journal editors) should adopt my colleagues’ advice from the beginning: Don’t ask about novelty, ask about insights. “What new insight is this paper presenting?” is a much better question than “Were you the first to find what you found?”