This is a complex topic and I think Gelman (the linked poster) is either misinterpreting and/or confused by Kaufman and Glǎveanu (the article he's discussing). Just for some context, I agree with Gelman and KG both in their main arguments.
There's different issues colliding in the open science movement. One is what you're referring to, the fact that scarcity is gone. Combined with the overcrowding and hypercompetitiveness of science, you not only have what you are referring to, a decrease in verdicality, you also have a decrease in signal-to-noise in general. So the nonsense increases, but so do the traditional signals to quality. So nonsense appears in high-profile journals, and very quality work appears in low-profile journals or even as "unpublished" pieces.
The other problem though, what my colleagues refer to as the "science police", is an increasing tendency for certain groups to argue that a certain set of practices are not only good, but necessary for "good" science, and by implication, everything that does not is "bad" science, in a black-and-white kind of way.
For one thing, not all problems are with replication. Nonsense can replicate well, and very important legitimate phenomena can be difficult to replicate. If something is really not replicable at all, that's a problem, but replicability per se is only one part of scientific progress, and it comes in degrees with various causes.
It's also much more difficult to determine what is replicable sometimes than it might seem on the surface. Replicate what? What's important to replicate? How? Sometimes this is clear, but other times it is not.
Also, when you really delve into it, there's not really a good rationale for what, exactly, are the important ingredients for open science, or why. For example, is it really necessary to have preregistered studies? What's to keep someone from preregistering but then silently declining to publish null results? Or to "preregister" something they've already collected? If an important unanalyzed pre-existing dataset becomes available, is that "tainted" because it wasn't preregistered? Is it important to preregister, or just to make the data openly available? Is it better to use modeling to identify anomalies in studies, or to rely on preregistration? These issues aren't always clear.
I think there's a sense sometimes that the open science movement is not only trying to dismantle a broken system run by an established elite, but to replace it dogmatically with a new system run by a new elite, with its own imperfect rules. Already I've seen misuses of open science guidelines used to bully and discredit legitimate work (for example, by suggesting that someone is hiding something by not sharing data, when the data contains protected healthcare information and would be accessible to them anyway if they would just go through proper channels). This is tricky to discuss, as you might imagine, so it comes out in pieces like KG's piece. Gelman is asking "why not publish everything", which is responding (I think) to something different from what KG are responding to. Maybe I'm misreading KG, but I think they might also argue "why not publish everything"; they just have a different group they're addressing when they would say that.
As a psychologist what I've grown outraged by is the hypocrisy of current approaches to suicide, in that they are all focused on stopping suicide, but not the pain that drives it.
We talk about taking away guns and pills, putting up fences around parking ramps and bridges, monitoring social media, and so forth and so on. But we do nothing to address the psychosocial ills that drive the suicidal person to their state.
Labeling the suicidal as "mentally ill" does nothing but allow the living to wash their hands of the accumulated insults that drive someone to where they are, to feel good and then walk away from the most difficult issues to address. It places the blame on the person who is suicidal. It's not about protecting the suicidal, it's about protecting the living from guilt.
Sure, there are many people who obviously suffer from some sort of biological insult, but there is also a large grey area of those who have some kind of handicap, who are then thrust into horrific circumstances on top of that. And there are those who just get a bad lot in life.
These issues are spilling over into euthanasia in a way that's starkly highlighted by this case.
I'm not saying healthcare professionals should be forced into something they see as murder, or murkily close to it. What I'm saying is that as a society, our focus should be less on preventing people from accessing the means of suicide -- or euthanasia as the label may be -- and more on the sources of pain that drives them to it.
There's different issues colliding in the open science movement. One is what you're referring to, the fact that scarcity is gone. Combined with the overcrowding and hypercompetitiveness of science, you not only have what you are referring to, a decrease in verdicality, you also have a decrease in signal-to-noise in general. So the nonsense increases, but so do the traditional signals to quality. So nonsense appears in high-profile journals, and very quality work appears in low-profile journals or even as "unpublished" pieces.
The other problem though, what my colleagues refer to as the "science police", is an increasing tendency for certain groups to argue that a certain set of practices are not only good, but necessary for "good" science, and by implication, everything that does not is "bad" science, in a black-and-white kind of way.
For one thing, not all problems are with replication. Nonsense can replicate well, and very important legitimate phenomena can be difficult to replicate. If something is really not replicable at all, that's a problem, but replicability per se is only one part of scientific progress, and it comes in degrees with various causes.
It's also much more difficult to determine what is replicable sometimes than it might seem on the surface. Replicate what? What's important to replicate? How? Sometimes this is clear, but other times it is not.
Also, when you really delve into it, there's not really a good rationale for what, exactly, are the important ingredients for open science, or why. For example, is it really necessary to have preregistered studies? What's to keep someone from preregistering but then silently declining to publish null results? Or to "preregister" something they've already collected? If an important unanalyzed pre-existing dataset becomes available, is that "tainted" because it wasn't preregistered? Is it important to preregister, or just to make the data openly available? Is it better to use modeling to identify anomalies in studies, or to rely on preregistration? These issues aren't always clear.
I think there's a sense sometimes that the open science movement is not only trying to dismantle a broken system run by an established elite, but to replace it dogmatically with a new system run by a new elite, with its own imperfect rules. Already I've seen misuses of open science guidelines used to bully and discredit legitimate work (for example, by suggesting that someone is hiding something by not sharing data, when the data contains protected healthcare information and would be accessible to them anyway if they would just go through proper channels). This is tricky to discuss, as you might imagine, so it comes out in pieces like KG's piece. Gelman is asking "why not publish everything", which is responding (I think) to something different from what KG are responding to. Maybe I'm misreading KG, but I think they might also argue "why not publish everything"; they just have a different group they're addressing when they would say that.