I think what these papers prove is my newer theory that organized science isn't scientific at all. It's mostly unverified claims by people rewarded for throwing papers out that look scientific, have novelty, and achieve policy goals of specific groups. There's also little review with dissent banned in many places. We've been calling it scientism since it's like a self-reinforcing religion.
We need to throw all of this out by default. From public policy to courtrooms, we need to treat it like any other eyewitness claim. We shouldn't beleive anything unless it has strong arguments or data backing it. For science, we need the scientific method applied with skeptical review and/or replication. Our tools, like statistical methods and programs, must be vetted.
Like with logic, we shouldn't allow them to go beyond what's proven in this way. So, only the vetted claims are allowed as building blocks (premises) in newly-vetted work. The premises must be used how they were used before. If not, they are re-checked for the new circumstances. Then, the conclusions are stated with their preconditions and limitations to only he applied that way.
I imagine many non-scientists and taxpayers assumed what I described is how all these "scientific facts" and "consensus" vlaims were done. The opposite was true in most cases. So, we need to not onoy redo it but apply scientific method to the institutions themselves assessing their reliability. If they don't get reliable, they loose their funding and quickly.
(Note: There are groups in many fields doing real research and experimental science. We should highlight them as exemplars. Maybe let them take the lead in consulting for how to fix these problems.)
> We need to throw all of this out by default. From public policy to courtrooms, we need to treat it like any other eyewitness claim.
If you can't trust eyewitness claims, if you can't trust video or photographic or audio evidence, then how does one Find Truth? Nobody really seems to have a solid answer to this.
It's specific segments of people saying we can't trust eyewitness claims. They actually work well enough that we run on them from childhood to adulthood. Accepting that truth is the first step.
Next, we need to understand why that is, which should be trusted, and which can't be. Also, what methods to use in what contexts. We need to develop education for people about how humanity actually works. We can improve steadily over time.
On my end, I've been collecting resources that might be helpful. That includes Christ-centered theology with real-world application, philosophies of knowledge with guides on each one, differences between real vs organized science, biological impact on these, dealing with media bias (eg AllSides), worldview analyses, critical thinking (logic), statistical analyses (esp error spotting), writing correct code, and so on.
One day, I might try to put it together into a series that equips people to navigate all of this stuff. For right now, I'm using it as a refresher to improve my own abilities ahead of entering the Data Science field.
> It's specific segments of people saying we can't trust eyewitness claims.
Scientists that have studied this over long periods of times and diverse population groups?
I've done this firsthand - remembered an event a particular way only to see video (in the old days, before easy video editing) and find out it... didn't quite happen as I remembered.
That's because human beings aren't video recorders. We're encoding emotions into sensor data, and get blinded by things like Weapon Focus and Selective Attention.
Much of what many learned about life came from their parents. That included lots of foundational knowledge that was either true or worked well enough.
You learned a ton in school from textbooks that you didn't personally verify.
You learned lots from media, online experts, etc. Much of which you couldn't verify.
In each case, they are making eyewitness claims that are a mix of first-hand and hearsay. Many books or journals report others' claims. So, even most education involves tons of hearsay claims.
So, how do scientists raised, educated, and informed by eyewitness claims write reports saying eyewitness testimony isn't reliable? How do scientists educated by tons of hearsay not believe eyewitness testimony is trustworthy?
Or did they personally do the scientific method on every claim, technique, machine, circuit, etc they ever considered using? And make all of it from first principles and raw materials? Did they never believe another person's claims?
Also, "scientists that have studied this over long periods of times and diverse population groups" is itself an eyewitness claim and hearsay if you want us to take your word for it. If we look up the studies, we're believing their eyewitness claims on faith while we've validated your claim that theirs exist.
It's clear most people have no idea how much they act on faith in others' word, even those scientists who claim to refute the value of it.
Leslie Lamport came up with a structured method to find errors in proof. Testing it on a batch, he found most of them had errors. Peter Guttman's paper on formal verification likewise showed many "proven" or "verified" works had errors that were spottes quickly upon informal review or testing. We've also see important theories in math and physics change over time with new information.
With the above, I think we've empirically proven that we can't trust mathmeticians more than any other humans We should still rigorously verify their work with diverse, logical, and empirical methods. Also, build ground up on solid ideas that are highly vetted. (Which linear algebra actually does.)
The other approach people are taking are foundational, machine-checked, proof assistants. These use a vetted logic whose assistant produces a series of steps that can be checked by a tiny, highly-verified checker. They'll also oftne use a reliable formalism to check other formalisms. The people doing this have been making everything from proof checkers to compilers to assembly languages to code extraction in those tools so they are highly trustworthy.
But, we still need people to look at the specs of all that to see if there are spec errors. There's fewer people who can vet the specs than can check the original English and code combos. So, are they more trustworthy? (Who knows except when tested empirically on many programs or proofs, like CompCert was.)
A friend of mine was given an assignment in a masters-level CS class, which was to prove a lemma in some seminal paper (It was one of those "Proof follows a similar form to Lemma X" points).
This had been assigned many times previously. When my friend disproved the lemma, he asked the professor what he had done wrong. Turns out the lemma was in fact false, despite dozens of grad students having turned in "proofs" of the lemma already. The paper itself still stood, as a weaker form of the lemma was sufficient for its findings, but still very interesting.
That comment sounds like the environment causes bad behavior. That's a liberal theory refuted consistently by all the people in bad environments who choose to not join in on the bad behavior, even at a personal loss.
God gave us free will to choose good or evil in various circumstances. We need to recognize that in our assessments. We must reward good choices and address bad ones (eg the study authors'). We should also change environments to promote good and oppose evil so the pressures are pushing in the right direction.
It might be a custom chip with all SRAM, no DRAM, that they normally use for AI acceleration. Running Firefox and PDF's on the side would be a nice, value add.
A one-way link (data diode) transmits it to a box with simplified hardware (eg RISC architecture). The box has a dedicated monitor and keyboard. Once you're finished, you sell the box on Craiglist. Then, buy a new, sealed replacement from Best Buy.
Pay per view was an expensive, business model for cable. For PDF's, it's even more expensive.
Note: It's more convenient than full, per-app, physical security.
Part of replication is skeptical review. That's also part of the scientific method. If we're not doing thorough review and replication, we're not really doing science. It's a lot of faith in people incentivized to do sloppy or dishonest work.
Edit: I just read your article linked upthread. It was really good. I don't think we disagree except I say we need to attempt the steps of science wherever sensible and there's human/political problems trying to corrupt them. I try to seperately address those by changing hearts with the Gospel of Jesus Christ. (Cuz self-interest won't fix science.)
So, we need the replications. We also need to address whatever issues would pop up with them.
Yes, that's true. In theory, by the time it gets to the replication stage a paper has already been reviewed. In practice a replication is often the first time a paper is examined adversarially. There might be a useful form of hybrid here, like paying professional skeptics to review papers. The peer review concept academia works on is a very naive setup of the sort you'd expect given the prevailing ideology ("from each according to their ability, to each according to their needs"). Paying professionals to do it would be a good start, but only if there are consequences to a failed review, which there just aren't today.
"There might be a useful form of hybrid here, like paying professional skeptics to review papers."
This is how the scientific method is described. It's what much of the public thinks their money is paying for. So, I'm definitely for doing it for real or not calling it science.
Even the amount of review I saw you do on papers on your blog seems to exceed what much peer review is doing. So, how can we treat things as science if that aren't even meeting that standard, much less replication?
Do those crops contribute to the negative numbers reported since most people don't buy biofuel? Or does it contribute something positive to the numbers with government subsidies guaranteeing returns?
I haven't studied the economics of the biofuel farming.
Corn is turned into ethanol, and is then blended with gasoline. The US consumes ~14B gallons of ethanol per year. It’s a net negative because it’s carbon and water intensive and farmers advocate for more ethanol than is necessary as a subsidy via government mandate.
They described a lot of data. Then, toward the end, they say:
"These loss estimates reflect national averages; actual costs of production and returns vary by region, management decisions and ownership structure. For example, producers who own their farmland may face lower total costs by avoiding cash rental expenses, resulting in higher returns."
So, can we trust this to say what it appears to be saying? Or might it be meaningless like many broad averages, and we should use more specific data that includes supplier behaviors?
Americans using foreign inputs or buying from companies that do. If you phrase it like that, then there's obvious solutions to reduce the effects of tariffs.
We need to throw all of this out by default. From public policy to courtrooms, we need to treat it like any other eyewitness claim. We shouldn't beleive anything unless it has strong arguments or data backing it. For science, we need the scientific method applied with skeptical review and/or replication. Our tools, like statistical methods and programs, must be vetted.
Like with logic, we shouldn't allow them to go beyond what's proven in this way. So, only the vetted claims are allowed as building blocks (premises) in newly-vetted work. The premises must be used how they were used before. If not, they are re-checked for the new circumstances. Then, the conclusions are stated with their preconditions and limitations to only he applied that way.
I imagine many non-scientists and taxpayers assumed what I described is how all these "scientific facts" and "consensus" vlaims were done. The opposite was true in most cases. So, we need to not onoy redo it but apply scientific method to the institutions themselves assessing their reliability. If they don't get reliable, they loose their funding and quickly.
(Note: There are groups in many fields doing real research and experimental science. We should highlight them as exemplars. Maybe let them take the lead in consulting for how to fix these problems.)
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