My take on the self correction is, that later research usually cannot be build on wrong results. So over the decades or centuries the correct results prevail.
I'm not constructing a full differential equation model for an HN comment, but I'm pretty sure any realistic model of likelihood of detection of the falseness of past research and other similar parameters would produce a surprisingly sharp cutoff when looked at over time, above which the science does tend to self-correct and below which it disintegrates into endlessly recursive garbage drowning out any real science being produced. And I'm pretty sure a number of fields are below that threshold right now. That doesn't mean the problem is permanent... the parameters can be changed over time, too, but the problem does get worse and harder to fix if allowed to accrue too much.
AFIK, it does not seem to happen much with mainstream medicinal research. Many layers of fraud/incompetence/assumptions for centuries with no correction in sight. Of course there are certain sub-areas that may have advanced. In fact it's the modern day religion, having replaced the god centric religion of yesteryears.
Otherwise people will happily go along with BS.
It often doesn't happen. The reasons seem to vary by field. We do see it in a few very hard fields like materials science.
In the softer end of social sciences like anthropology, education or the humanities it's obvious why not: they don't make empirically testable claims about reality anyway so there's nothing to replicate. This is the Arday problem, where he was publishing what they call auto-ethnography: effectively just blog posts about the author's own feelings. He even interviewed himself in the third person.
In the harder social sciences it's because nothing actually builds on anything else (see "A problem in theory" [1]). Psychology and related fields like sociology, criminology, etc are basically just a pile of random ideas pooped out in brainstorming sessions. There are hardly any overarching theories generating testable predictions, so studies test whatever random claims the researcher thinks is interesting enough to get in the news. Everything is just rocks lying on the ground rather than towers of understanding, so when one paper is overturned it has no impact on anything else. Citation counts obscure this fact, but do some reverse-citation checks and you'll find a lot of citations are worthless or outright invalid.
In modeling based fields like economics, epidemiology or climatology, it's because they can't put the thing they study in a lab and do high speed experimentation. "Replication" comes to mean repeating the same analysis on the same data set. Often they literally just run the same program on the same input file, get the same result and call it replicated - or sometimes they call it replicated even if the outputs don't match! See [2]. But the program is just a set of assumptions, not real discoveries, so building more papers on top of that program's output doesn't detect invalidity. It will all replicate in some very narrow technical sense without that proving anything useful.
Sometimes outsiders notice what's happening but over time academia has built up a complex web of lore that protects them psychologically. For example, they routinely reject or ignore feedback from outside their field on the basis that it doesn't come from "experts", meaning themselves, or it comes from "right wing" people, meaning anyone who isn't an academic or close political ally.
[1] https://www.nature.com/articles/s41562-018-0522-1
[2] https://dailysceptic.org/2020/05/06/code-review-of-fergusons...
That's because those fields do still make objective empirical predictions about the world, which might take years to be testable but are still ultimately testable. For example, it's been convincingly shown that climate models from decades ago predicted our current climate pretty well. [0]
These fields are more comparable to subfields of physics which only get to do big experiments a handful of times, e.g. because they can only observe so many planets/supernovae/universes, or because they have to spend decades building a new billion-dollar machine to test new hypotheses.
We should also not confuse these arguments with uselessness - soft social science work can still be societally valuable without being easily empirically testable.
[0] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/201...
Modern (computational) epidemiology is rife with unscientific practices. They ignore data that shows a model was invalidated so the fact they make testable predictions isn't really useful. They also engage in a lot of circular reasoning, buggy coding and logical fallacies. During COVID I wrote a whole report on this topic for some politicians [1]. But the biggest issue is "A problem in theory" again - epidemiologists conflate fitting a curve in R with developing a hypothesis, so the field is overrun with overfit models that aren't based on any refinable theory of disease, just misuses of statistics. Even if you prove a paper's predictions were wrong it changes nothing because nothing built on it anyway.
The problem in climatology is that when the models don't fit the data they just change the data and claim victory, e.g.
September 2013: https://www.spiegel.de/international/world/climate-scientist...
June 2015: https://www.nature.com/articles/nature.2015.17700
That is... not remotely true, nor supported by either of the links you shared. (Nor by any cursory look at the state of the world today, with well-anticipated Himalayan glacial floods as front-page news.) You therefore appear to be leaping over the line from healthy skepticism to irrational conspiratorial thinking, so I'm going to stop engaging, sorry.
https://www.theweek.in/news/world/2026/08/28/new-satellite-i...
The two links I give show the problem in action. In 2013 the IPCC was concerned because temperatures had gone sideways for a decade. Der Spiegel's first sentence is "Data shows global temperatures aren't rising the way climate scientists have predicted" and the next says they were trying to "hush it up". In 2015 climatologists announced a new temperature record that replaced the entire 21st century with different data, as reported by Nature.
That's an unambiguous sequence of events. You can't make statements about replicability in an environment where they routinely decide their already published data is wrong post-hoc, without retracting the papers built on that data.
Because Down Votes Are Truth.
And that, ladies n gennelman, is How Science Works.
That’s how we end up with Jason Arday: Up Votes.
Disengaging is sticking your fingers in your ears and blurting out “la la la la I can’t hear you!”
And calling that a win.
The problem isn’t science, the problem is that there are adults with the maturity of a four year old acting as sciences hype man.
The issue is that pandemics are extremely fat-tailed in terms of fatalities [3]. You need to collect a shit tons of data to estimate any reliable parameters and no one has time for that. The only thing you need to know is: Is this a super contagious deadly disease? If yes, lock everything down! This is pretty much what they did in Asia, and countries like Vietnam had zero cases for months and was open while the rest of the world was closed.
How to decide a disease is deadly and super contagious? You need disease experts (who most likely also academics) who understand the nature of the disease, and not better modellers and programmers as suggested in [2]. The issue is that western countries will never let any expert to make that call, so a lot of convincing needs to be done through complicated modelling and real life suffering.
After all, why suffer if it isn’t actually necessary?
Asia had recent experience with the first SARS, which had very real and extremely high fatality rates, so for them it was a relatively easy (not an easy) call.
For the west, when was the last time we had a real pandemic that actually killed a lot of people? Almost a century ago.
Somewhat ironically because the west listened to experts for a very long time.
Notably, the last time it had a major issue also coincided with a world war.
The word you're looking for is "triangulation" - https://en.wikipedia.org/wiki/Triangulation_(social_science)
As long as you can find [1] a few [2] citations [3] that kind of all point in the same kind of direction then you're good!