Friday links: the knowledge machine, Fat Bear Week, and more (UPDATED)

Also this week: how much would we have to pay you to research something else, faculty jobpocalypse, Excel vs. English public health, and more.

From Jeremy:

The 2020 MacArthur Fellows (colloquially known as the “genius grants”) include evolutionary geneticist Nels Elde.

Wow: Brown University Economics is suspending all graduate admissions for the 2021-2022 academic year due to Covid-19. (ht Marginal Revolution) UPDATE: the link has been taken down. Thank you to a commenter for pointing this out. I do not know if this means the suspension has been lifted, or if there was never a suspension in the first place (e.g., maybe somebody at Brown prematurely announced a policy option that was under discussion but hadn’t actually been decided upon?)

This looks interesting. Here’s the abstract:

This paper identifies the degree to which scientists are willing to change the direction of their work in exchange for resources. Data from the National Institutes of Health are used to estimate how scientists respond to targeted funding opportunities. Inducing a scientist to change their direction by a small amount—to work on marginally different topics—requires a substantial amount of funding in expectation. The switching costs of science are large. The productivity of grants is also estimated, and it appears the additional costs of targeted research may be more than offset by more productive scientists pursuing these grants.

I haven’t read it yet, just passing it along in case you want to check it out.

Sticking with the topic of why scientists operate as they do, here’s a long New Yorker piece on philosopher of science Michael Strevens’ new book The Knowledge Machine. Sounds very interesting. One question I have after reading the linked article: sounds like the book mostly draws on the same small set of famous cases that philosophers of science have long mulled over. Continental drift. Eddington’s solar eclipse observations. Spontaneous generation. Etc. Are philosophers of science misled about whether/how/why science works by focusing on those famous cases? We asked in an old post how many model systems ecology needs. Analogously, how many focal case studies does philosophy of science need?

Athene Donald speculates on why women applicants tend to apply for, and receive, smaller UKRI grants than do men. I was most struck by this passage:

As a final remark, I’d like to highlight one of the steps the EPSRC are apparently congratulating themselves on, namely that they have increased the number of women on panels and as panel chairs. It sounds good but having 30% of women on panels plus 31% acting as panel chairs when they are only at 17% in the pool of researchers, means women are expected to give up – on average – twice as much time as men to this community service. There is no doubt that sitting on panels is wonderful experience (although I sometimes used to think I learned more about what a bad grant looked like, rather than a good one, which only used to throw me into paralysis when writing my own).

It is great that more women are being given the opportunity to see how the system works. But, simultaneously, they are also being asked to give up time to this service when they could have been writing their own grants. If the evidence supported the view that having more women on panels led to fairer decisions the case would be stronger. But all the evidence I saw during my time at the ERC** showed, across the board of all disciplines, a larger proportion of women on a panel tended to lead to women being more disadvantaged rather than less. I fear that sticking more women on panels may smack more of doing something that looks like a quick fix rather than resolving the issues the current data is showing up.

I hate it when I’m right. (Obviously, you shouldn’t be impressed by my prescience; everyone knew this bad news was coming. But it’s still bad news of course.)

Varieties of black political philosophy. Useful introductory-level roadmap to lines of thought that underpin much political discussion, online and off.

Remember kids English public health agency, there’s an upper limit to the number of rows in an Excel file. 😦 (ht @kjhealy)

Assume a spherical cow bear. 🙂

15 thoughts on “Friday links: the knowledge machine, Fat Bear Week, and more (UPDATED)

  1. The Strevens article gets at a truth that most scientists may not have explicitly acknowledged but matches their intuition. That what separates science from non-science can’t be captured by describing the historical pattern of science (Kuhn) or advocating for a narrow philosophical approach (Popper). What separates science from nonscience is (1) an absolute belief in an objective ‘truth’ – that there is a real world that exists apart from how humans perceive it, and (2) that empirical evidence is the arbiter of what that objective truth is. I would prefer science to be defined by something other than a person’s beliefs and intentions – but I don’t think it is. Based on a superficial read of the New Yorker article, I think Strevens is on the right track but it’s a track that most scientists are already on.

    • Jeff – interesting question about what most scientists think. I teach a grad stats class and speak a week on philosophy of science and inference. When I pose the “demarcation question” from Popper & Lakatos (what distinguishes science from pseudoscience), invariably the first answer is “the scientific method”. I then unpack what is “the scientific method” (everybody from multiple countries all learned the same 4 step scientific method in grade school) and whether science really always follow this (the answer is no of course). So I think with a lot of prodding people get to the point you state (it may represent their inner most beliefs), but I think we scientists have got a lot of ornamental edifices built on top of that core truth that we are pretty quick to regurgitate.

  2. Brian, I’ll push back a little on the idea that community consensus is a necessary piece of the demarcation line – if you are trying to approach an objective truth using empirical evidence does it make it more or less ‘science’ if most people agree or disagree with your conclusions? Isn’t your commitment to finding the ‘truth’ using empirical evidence, the defining ingredient? I agree that the faith a layperson has in the conclusions should depend on the consensus – but I don’t think the prevailing consensus is relevant to the question of whether somebody is doing science versus non-science.

    • Well I’m coming at this from a Lakatosian point of view. But its not true that empirical data alone is decisive. Science is littered with examples where the data seem to contradict the theory and yet people still believe the theory (and in many of those cases where people chose the theory over the data they were wrong and in many other cases they were right). So there is judgment going on about whether the data or the theory should remain standing. And we cannot trust any one person (or research group’s) judgment about this. Hence the need for a community component to science.

      That said the individual can be right and the community wrong (Wegener with continental drift and Marshall with a bacterial origin for stomach ulcers are canonical examples). But in what sense were either of these individuals influencing “science” or able to influence policy, the textbooks or whatever metric until they had convinced their own scientific communities? And you have to match your Wegeners and Marshalls with cold fusion, Duke’s ESP lab, and plenty of other examples of individuals who had convinced themselves that something that was untrue was “empirically supported”.

      To my mind science is only half done until you’ve convinced your peers. That step is an important part of the rigor of science vs the ability for an individual to self-delude.

  3. OK Brian, I’m convinced on almost all of this. But, ultimately doesn’t the final decision have to be based on data? Data can mislead us – either because of sampling problems, or biases in the measurement or believing the data support inferences that they don’t (and probably a dozen other reasons), But, weren’t all of those controversies ultimately resolved by data?

    • Agreed scientific controversies get solved by data. But often it is data that creates scientific controversies (the Michelson-Morley experiment infamously took 25 years and the genius of Einstein before the proper interpretation – ether doesn’t exist – was made). And we don’t collect data independent of what current scientific consensus is (and probably couldn’t interpret it if we did – how would you measure energy without a scientific consensus of what energy is and how to measure it? or measure competition in ecological systems without a similar consensus?).

      I see community opinion about what we know and data as in a feedback cycle.

      I don’t think we’re far apart – I just think its impossible for data to not have to pass through a subjective interpretation and believe that in science that interpretation best happens in a community informed by prior scientific knowledge.

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