121–130 of 215 posts
Earlier quoted context omitted.
Whoosh.
not everyone is in the same mindset, no need to be a jerk about it.
The core problem here is that universities think that citation statistics are a useful metric to evaluate the quality of the work of a scientist. There's plenty of evidence that this is not the case or that even the reverse may be the case [1], but this idea refuses to die. [1]
It sucks as a metric but it does have some rough correlation in most cases, and I'm not aware of any better easily measurable metric - if you have one in mind, it'd be great to hear. The alternative of having a bureaucrat "simply judge quality" IMHO is even worse, even less objective, and even more prone to being gamed. The main problem is that there is an objective need (or desire?) by various stakeholders to have s…
Earlier quoted context omitted.
It sucks as a metric but it does have some rough correlation in most cases, and I'm not aware of any better easily measurable metric - if you have one in mind, it'd be great to hear. The alternative of having a bureaucrat "simply judge quality" IMHO is even worse, even less objective, and even more prone to being gamed. The main problem is that there is an objective need (or desire?) by various stakeholders to have s…
I think a problem here is Goodhart's law: "When a measure becomes a target, it ceases to be a good measure." [1] And it seems like there's an element of the streetlight effect [2], too; sometimes a bad metric really is worse than no metric. Also, I really question your notion that people outside a field should be able to evaluate the quality of someone's work, especially in academia, where the whole point is to be we…
It's about the question of resource allocation. Pretty much every subfield of academia is a net consumer of resources, i.e. someone outside of that subfield is funneling resources to it. That someone - no matter if it's a university, or some foundation, or a gov't agency, or a philantrophist - needs to make a decision on how to allocate resources. And, in general, they honestly want to make a good, informed decision on which projects and researchers to support; but nonetheless they have to make a decision according to some criteria. So there's no choice of "no metric", there will always be a metric and we can only argue that it should be better. And the answer to "why anybody would willingly submit themselves to that" is that duh, you don't get a choice - you can suggest a better method to fulfil their goals of allocating resources in a way that is (also in their opinion) fair and objective; but you can't get around the fact that scientists are generally funded by nonscientists. And they need(or want) to make decisions.
They could delegate that, but that doesn't solve the question about the criteria - if they delegate that to universities, they still have to decide on how to allocate between departments; if they delegate that to scientist councils uniting all the departments in the country working on some subfield, they have to decide on how to allocate between the different organizations. So no matter what, you have to compare not only quality of similar scientists, but also of dissimilar scientists working in different (sub)fields. And delegation doesn't absolve you from responsibility, so if the money is (or looks!) wasted, then that's a failure - so when you delegate, you want to require them to use objective criteria. Which is hard - I could tell you which researchers in my subfield are doing excellent work and which are useless; but if I had to justify these decisions, to demonstrate why they're not just my bias because of politics/liking certain methods/gender/ethnicity/etc then it would actually be tricky; and I think that I'd actually reach out for these metrics. And I'm quite certain that the metrics (for the people that I have in mind) would agree with my subjective opinion; on average, the great research gets cited much more and is in higher-ranking venues; while the lousy stuff gets no citations apart from the author's only grad student.
Also, there's a lack of trust (IMHO not totally unwarranted). You could get a bunch of experts who are qualified to evaluate who gets what amount of resources, you can't rely on them actually doing so - if we take spiders as a totally random example, in general you're qualified to distinguish which spider research is good and which is useless only if you actually work on spider research, most likely in one of these teams - and the expected result is nepotism, allocating resources based on purely (intra-field) political reasons. And who'd decide on how to split resources between spider research and bird research? Do you expect the spider guys and bird guys to reach a consensus? Or would it go to whatever field the dean is in? This is a big problem even currently, and a big part of why the metrics are being gamed - but at least metrics are something that require effort to game and can't be gamed totally; if we'd do away with them, then we'd be left with absolutely arbitrary political allocation, which would be even worse.
So at the end of the day "they" need some way to transform the only reasonable source of truth - actual peer-review - to something that "non-peers" can use to judge what the the aggregate of that peer review says. That need is IMHO not negotiable, I really believe that they do actually need it - they don't want to do resource allocation totally arbitrarily, they want to do it well, they need (because of external pressures) objectivity and accountability, and currently this (journal rankings, bibliometerics, etc) is the best what we have suggested to summarize the results of that peer review.
If I had to write a law draft for a better process of allocating resources, what should be written in it?
Earlier quoted context omitted.
Most scientists work on topics that are quite niche. Most of those topics lead to nothing. A lot of good research took years to ripen enough to be of actual value. A lot of popular topics started out in a niche. Most of mathematics took dozens of years to fully come to fruitition. Can you decide beforehand which one will be the next big thing? Today, most scientists go for the popular topics and whatever is on the go…
So to answer the question, you are so brilliant no one else has stumbled on this question
If you narrow yourself to a specific niche well enough, you'll see the same names in citations. To be fair, the areas I dig into don't feel nearly as competitive as say, physics, which I couldn't make heads or tails of. The whole reason the internet and wikis took off is we were very liberal in how we linked. If we disallowed inbound citations, wouldn't it be a lot harder to backtrack and grasp contextual underpinnin…
Earlier quoted context omitted.
It sucks as a metric but it does have some rough correlation in most cases, and I'm not aware of any better easily measurable metric - if you have one in mind, it'd be great to hear. The alternative of having a bureaucrat "simply judge quality" IMHO is even worse, even less objective, and even more prone to being gamed. The main problem is that there is an objective need (or desire?) by various stakeholders to have s…
Why not simply use replication as a measure? Have your studies been replicated? How many other studies have you replicated? Would both help solve the replication crisis, and resolve this problem. Of course then you might have 10 000 studies replicating the same easy to do study... which is why the "score" should be reduced based on how many other times that study has been replicated.
An insightful study that's replicable (but has not yet been) is valuable. A lousy study that's been replicated five times (not because it's interesting, but because it was easy to do, and the replicators knew that they'd be rewarded for replicating anything) is not valuable.
A metric that says "number of studies" is IMHO even more arbitrary, more gameable, and more detached from actual value than citation count - which does have some notion that your study actually matters to other poeple; that it was worth writing that paper because someone read it.
The core problem here is that universities think that citation statistics are a useful metric to evaluate the quality of the work of a scientist. There's plenty of evidence that this is not the case or that even the reverse may be the case [1], but this idea refuses to die. [1]