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Internal motivation and acting according to your values is not necessarily a good thing. For example, repeat offenders are often internally motivated. They keep committing crime, because they don't fit in. And because their motivations are internal, incentives such as strict punishments have limited effect on their behavior.

Science selects actively against people who react strongly to incentives. The common and incentivized path is not doing science. Competitive sports are the opposite, as they appeal more to externally motivated people. From a scientist's point of view, the honest 99% of cyclists would absolutely dominate the race, as they ride 99% of the miles. Maybe they won't win, but winning is overrated anyway. Just like prestigious awards, vanity journals, and top universities are nice but ultimately not that important.

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dilap 13 hours ago | parent [-]

> Science selects actively against people who react strongly to incentives

I don't think this is true at all! If it were true, we would not have the reproducibility crises and various other scandals that we do, in fact, have.

Scientists are humans like any other, and respond to incentives.

Funding is a game -- you have to play the game in a way that wins to keep getting funding, so necessarily idealists that don't care about the rules of the game will be washed out and not get funding. It's in our collective interest, then, to make sure that winning the game equates to doing good science!

	▲	jltsiren 9 hours ago \| parent [-]
		In the almost 20 years I've done academic research, I've met thousands of scientists. Some of them have been involved in various scandals, but as far as I know, none of the scandals were about scientific integrity. When it comes to academic scandals, those involving scientific integrity seem to be rare. The reproducibility crisis seems to be mostly about applying the scientific method naively. You study a black box nobody really understands. You formulate a hypothesis, design and perform an experiment, collect data, and analyze the data under a simple statistical model. Often that's the best thing you can do, but you don't get reliable results that way. If you need reliability, you have to build models that explain and predict the behavior of the former black box. You need experiments that build on a large number of earlier experiments and are likely to fail in obvious ways if the foundations are not fundamentally correct. I'm pretty bad at getting grants myself, but I've known some people who are really good at it. And they are not "playing the game", or at least that's not the important part. What sets them apart is the ability to see the big picture, the attention to details, the willingness to approach the topic from whatever angle necessary, and vision of where the field should be going. They are good at identifying the problems that need to be solved and the approaches that will likely solve them. And then finding the right people to solve them.