Grad students don’t get to publish a thesis on reproduction. Everyone from the undergraduate research assistant to the tenured professor with research chairs are hyper focused on “publishing” as much “positive result” on “novel” work as possible
Prerequisite required by who, and why is that entity motivated to design such a requirement? Universities also want more novel breakthrough papers to boast about and to outshine other universities in the rankings. And if one is honest, other researchers also get more excited about new ideas than a failed replication that may for a thousand different reasons and the original authors will argue you did something wrong, or evaluated in an unfair way, and generally publicly accusing other researchers of doing bad work won't help your career much. It's a small world, you'd be making enemies with people who will sit on your funding evaluation committees, hiring committees and it just generally leads to drama. Also papers are superseded so fast that people don't even care that a no longer state of the art paper may have been wrong. There are 5 newer ones that perform better and nobody uses the old one. I'm just stating how things actually are, I don't say that this is good, but when you say something "should" happen, think about who exactly is motivated to drive such a change.
> Prerequisite required by who, and why is that entity motivated to design such a requirement?
Grant awarding institutions like the NIH and NSF presumably? The NSF has as one of its functions, “to develop and encourage the pursuit of a national policy for the promotion of basic research and education in the sciences”. Encouraging the replication of research as part of graduate degree curricula seems to fall within bounds. And the government’s interest in science isn’t novelty per se, it’s the creation and dissemination of factually correct information that can be useful to its constituents.
The commenter I was replying to wanted it to be a prerequisite for a degree, not for a grant. Grant awarding institutions also have to justify their spending to other parts of government and/or parliament (specifically, politicians). Both politicians and the public want to see breakthrough results that have the potential to cure cancer and whatnot. They want to boast that their funding contributed to winning some big-name prize and so on. You have to think with the mind of specific people in specific positions and what makes them look good, what gets them praise, promotions and friends.
> And the government’s interest in science isn’t novelty per se, it’s the creation and dissemination of factually correct information that can be useful to its constituents.
I think Arxiv and similar could contribute positively by listing replications/falsifications, with credit to the validating authors. That would be enough of an incentive for aspiring researchers to start making a dent.
But that seems almost trivially solved. In software it's common to value independent verification - e.g. code review. Someone who is only focused on writing new code instead of careful testing, refactoring, or peer review is widely viewed as a shitty developer by their peers. Of course there's management to consider and that's where incentives are skewed, but we're talking about a different structure. Why wouldn't the following work?
A single university or even department could make this change - reproduction is the important work, reproduction is what earns a PhD. Or require some split, 20-50% novel work maybe is also expected. Now the incentives are changed. Potentially, this university develops a reputation for reliable research. Others may follow suit.
Presumably, there's a step in this process where money incentivizes the opposite of my suggestion, and I'm not familiar with the process to know which.
Is it the university itself which will be starved of resources if it's not pumping out novel (yet unreproducible) research?
> In software it's common to value independent verification - e.g. code review. Someone who is only focused on writing new code instead of careful testing, refactoring, or peer review is widely viewed as a shitty developer by their peers.
That is good practice
It is rare, not common. Managers and funders pay for features
Unreliable insecure software sells very well, so making reliable secure software is a "waste of money", generally
> Presumably, there's a step in this process where money incentivizes the opposite of my suggestion, and I'm not familiar with the process to know which.
> Is it the university itself which will be starved of resources if it's not pumping out novel (yet unreproducible) research?
Researchers apply for grants to fund their research, the university is generally not paying for it and instead they receive a cut of the grant money if it is awarded (IE. The grant covers the costs to the university for providing the facilities to do the research). If a researcher could get funding to reproduce a result then they could absolutely do it, but that's not what funds are usually being handed out for.
Universities are not really motivated to slow down the research careers of their employees, on the contrary. They are very much interested in their employees making novel, highly cited publications and bringing in grants that those publications can lead to.
That.. still requires funding. Even if your lab happens to have all the equipment required to replicate you're paying the grad student for their time spent on replicating this paper and you'll need to buy some supplies; chemicals, animal subjects, pay for shared equipment time, etc.
We are on the comment section about an AI conference and up until the last few years material/hardware costs for computer science research was very cheap compare to other sciences like medicine, biology etc. where they use bespoke instruments and materials. In CS, up until very recently, all you needed was a good consumer PC for each grad student that lasted for many years. Nowadays GPU clusters are more needed but funding is generally not keeping up with that, so even good university labs are way underresourced on this front.
Enough people will falsify the replication and pocket the money, taking you back to where you were in the first place and poorer for it. The loss of trust is an existential problem for the USA.
Here's a work from last year which was plagiarized. The rare thing about this work is it was submitted to ICLR, which opened reviews for both rejected and accepted works.
You'll notice you can click on author names and you'll get links to their various scholar pages but notably DBLP, which makes it easy to see how frequently authors publish with other specific authors.
Some of those authors have very high citation counts... in the thousands, with 3 having over 5k each (one with over 18k).
Not in most fields, unless misconduct is evident. (And what constitutes "misconduct" is cultural: if you have enough influence in a community, you can exert that influence on exactly where that definitional border lies.) Being wrong is not, and should not be, a career-ending move.
If we are aiming for quality, then being wrong absolutely should be. I would argue that is how it works in real life anyway. What we quibble over is what is the appropriate cutoff.
There's a big gulf between being wrong because you or a collaborator missed an uncontrolled confounding factor and falsifying or altering results. Science accepts that people sometimes make mistakes in their work because a) they can also be expected to miss something eventually and b) a lot of work is done by people in training in labs you're not directly in control of (collaborators). They already aim for quality and if you're consistently shown to be sloppy or incorrect when people try to use your work in their own.
The final bit is a thing I think most people miss when they think about replication. A lot of papers don't get replicated directly but their measurements do when other researchers try to use that data to perform their own experiments, at least in the more physical sciences this gets tougher the more human centric the research is. You can't fake or be wrong for long when you're writing papers about the properties of compounds and molecules. Someone is going to come try to base some new idea off your data and find out you're wrong when their experiment doesn't work. (or spend months trying to figure out what's wrong and finally double check the original data).
In fields like psychology, though, you can be wrong for decades. If your result is foundational enough, and other people have "replicated" it, then most researchers will toss out contradictory evidence as "guess those people were an unrepresentative sample". This can be extremely harmful when, for instance, the prevailing view is "this demographic are just perverts" or "most humans are selfish thieves at heart, held back by perceived social consensus" – both examples where researcher misconduct elevated baseless speculation to the position of "prevailing understanding", which led to bad policy, which had devastating impacts on people's lives.
(People are better about this in psychology, now: schoolchildren are taught about some of the more egregious cases, even before university, and individual researchers are much more willing to take a sceptical view of certain suspect classes of "prevailing understanding". The fact that even I, a non-psychologist, know about this, is good news. But what of the fields whose practitioners don't know they have this problem?)
Yeah like I said the soft validation by subsequent papers is more true in more baseline physical sciences because it involves fewer uncontrollable variables. That's why I mentioned 'hard' sciences in my post, messy humans are messy and make science waaay harder.
Well, this is why the funniest and smartest way people commit fraud is faking studies that corroborate very careful collaborators' findings (who are collaborating with many people, to make sure their findings are replicated). That way, they get co-authorship on papers that check out, and nobody looks close enough to realize that they actually didn't do those studies and just photoshopped the figures to save time and money. Eliezer Masliah, btw. Ironically only works if you can be sure your collaborators are honest scientists, lol.
There is actually a ton of replication going on at any given moment, usually because we work off of each other's work, whether those others are internal or external. But, reporting anything basically destroys your career in the same way saying something about Weinstein before everyone's doing it does. So, most of us just default to having a mental list of people and circles we avoid as sketchy and deal with it the way women deal with creepy dudes in music scenes, and sometimes pay the troll toll. IMO, this is actually one of the reasons for recent increases in silo-ing, not just stuff being way more complicated recently; if you switch fields, you have to learn this stuff and pay your troll tolls all over again. Anyway, I have discovered or witnessed serious replication problems four times --
(1) An experiment I was setting up using the same method both on a protein previously analyzed by the lab as a control and some new ones yielded consistently "wonky" results (read: need different method, as additional interactions are implied that make standard method inappropriate) in both. I wasn't even in graduate school yet and was assumed to simply be doing shoddy work, after all, the previous work was done by a graduate student who is now faculty at Harvard, so clearly someone better trained and more capable. Well, I finally went through all of his poorly marked lab notebooks and got all of his raw data... his data had the same "wonkiness," as mine, he just presumably wanted to stick to that method and "fixed" it with extreme cherry-picking and selective reporting. Did the PI whose lab I was in publish a retraction or correction? No, it would be too embarrassing to everyone involved, so the bad numbers and data live on.
(2) A model or, let's say "computational method," was calibrated on a relatively small, incomplete, and partially hypothetical data-set maybe 15 years ago, but, well, that was what people had. There are many other models that do a similar task, by the way, no reason to use this one... except this one was produced by the lab I was in at the time. I was told to use the results of this one into something I was working on and instead, when reevaluating it on the much larger data-set we have now, found it worked no better than chance. Any correction or mention of this outside the lab? No, and even in the lab, the PI reacted extremely poorly and I was forced to run numerous additional experiments which all showed the same thing, that there was basically no context this model was useful. I found a different method worked better and subsequently, had my former advisor "forget" (for the second time) to write and submit his portion of a fellowship he previously told me to apply to. This model is still tweaked in still useless ways and trotted out in front of the national body that funds a "core" grant that the PI basically uses as a slush fund, as sign of the "core's" "computational abilities." One of the many reasons I ended up switching labs. PI is a NAS member, by the way, and also auto-rejects certain PIs from papers and grants because "he just doesn't like their research" (i.e. they pissed him off in some arbitrary way), also flew out a member of the Swedish RAS and helped them get an American appointment seemingly in exchange for winning a sub-Nobel prize for research... they basically had nothing to do with, also used to basically use various members as free labor on super random stuff to faculty who approved his grants, so you know the type.
(3) Well, here's a fun one with real stakes. Amyloid-β oligomers, field already rife with fraud. A lab that supposedly has real ones kept "purifying" them for the lab involved in 2, only for the vial to come basically destroyed. This happened multiple times, leading them to blame the lab, then shipping. Okay, whatever. They send raw material, tell people to follow a protocol carefully to make new ones. Various different people try, including people who are very, very careful with such methods and can make everything else. Nobody can make them. The answer is "well, you guys must suck at making them." Can anyone else get the protocol right? Well, not really... But, admittedly, someone did once get a different but similar protocol to work only under the influence of a strong magnetic field, so maybe there's something weird going on in their building that they actually don't know about and maybe they're being truthful. But, alternatively, they're coincidentally the only lab in the world that can make super special sauce, and everybody else is just a shitty scientist. Does anyone really dig around? No, why would a PI doing what the PI does in 2 want to make an unnecessary enemy of someone just as powerful and potentially shitty? Predators don't like fighting.
(4) Another one that someone just couldn't replicate at all, poured four years into it, origin was a big lab. Same vibe as third case, "you guys must just suck at doing this," then "well, I can't get in contact with the graduate student who wrote the paper, they're now in consulting, and I can't find their data either." No retraction or public comment, too big of a name to complain about except maybe on PubPeer. Wasted an entire R21.
But the problem isn’t just funding, it’s time. Successfully running a replication doesn’t get you a publication to help your career.