From Preprints to Omniprints

Starting with ArXiv, the idea of preprints has been increasingly in popularity for some time. But now is the time for omniprints. Preprints were a good start. And Crossref has been indexing more and more preprints, with preprints outpacing journal articles by far (Crossref). There are a number of servers, including ArXiv and its derivatives, OSF’s preprint servers, ResearchGate, and more.

I rely exclusively on preprint servers for my publication, mostly out of spite for modern academia and its toxic nature. I absolutely refuse to pay a company so that they can profit off of my work. And honestly, if the goal of publishing is to communicate with other researchers, than traditional publications are not the answer, especially when they’re not open access.

But there’s an issue. A lot of people reject citation of preprints. They want to wait until there’s a “final” version. It’s not even that they’ll scrutinize it more heavily, but rather they will outright use the preprint nature of the paper to ignore it.

Of course, what matters isn’t whether a journal has decided to pick up an article — Wakefield taught us that—but rather what matters is that the content of the article is sound. And in order to determine whether that’s the case, a person has to read the article.

I do think that part of the problem is that the articles are called pre-prints. It’s right in the name: the article hasn’t been printed yet. It hasn’t been completed. That’s why we need to rename preprint servers, which have long since become far more than that, to something else. I’m not really sure what name we’ll end up using, but perhaps “omniprint” is the best option, as it implies “all prints” whether preprint or postprint, draft print, or final print.

Real Open Science

Related to omniprints is the idea of open access, where a journal lets anyone access the publication. I don’t see open access as real open science. It’s certainly a start. After all, if the goal of publishing is to communicate, we need to be able to read what’s being published! But it’s simply not enough. For one thing, publishing in open access journals is often very expensive, literally costing the author thousands of dollars! That’s why we need omniprint.

Actual Peer Review

Of course, in order to take full advantage of omniprint servers, they need to provide a number of tools to allow for an open peer review. Comment systems are useful, but they’re not a great way to quickly measure the quality of the paper. I think a tagging system might be useful, where people can anonymously tag a paper. Tags would probably have to include whether or not the paper is scholarly, if it justifies its position, if it needs improvement, and so on.

And that would be actual peer review. What we think of as peer review is really just one or two reviewers, who might be quite biased, along with an editor. How can we trust two or three people to make a decision about a paper, in an unbiased way? We can’t. That’s why we need omniprint.

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Ramblings on a Paraconsistent Reality

By alcanthro Leave a Comment Feb 17

There are two camps of science: the provisional verification camp, which was really the first to arise, in a formal sense, when Francis Bacon formulated “the scientific method.” The second camp arose when philosophers such as Kant and Hume realized that there was an issue with induction. They questioned why repeated observations, consistent with a given explanation, really provided any justification for the theory. This concern led to Karl Popper creating a new view of science, as a system of falsification. And that’s where we’ll start this discussion.

Logical Consistency

Consistency is at the heart of science. If we have one theory, and it says that another is false, we can conclude that at most one is true. So if we have a theory that’s well justified, and another that we know little about, if the first theory informs us that the second is false, we’re fairly confident in rejecting the second theory, at least for the time being. And whether we’re discussing the Baconian camp or the Popperian camp, logical consistency is still king.

The Issue

But what happens when two theories both seem true? That’s the case with general relativity and quantum mechanics. Both of these theories are very useful to us. Both have undergone numerous tests. And both of them have survived all of those tests. But there’s a catch. While these two theories generally mind their own business, there’s an area of physics where both theories make predictions.

You see, general relativity mostly involves big things, like planets and other objects moving through space. General relativity is what replaced Newtonian physics, and involves treating the universe as a smooth space-time manifold that can be warped by mass and energy.

Quantum mechanics usually involves really tiny things, like atoms and stuff that makes them up. It views the universe as being coarse rather than smooth. But in general, quantum mechanics does not make any predictions about types of things that on which general relativity informs us. And in that case, there’s no issue.

But in really strange areas, like near black holes, general relativity and quantum mechanics don’t play nice. They both make predictions and neither is reconcilable with the other. So as scientists know, we need a new theory that can properly work in both domains: a so called “grand unification.”

String theory, super-string theory, and many other theories have been proposed in order to extend quantum mechanics into the domain of large scale predictions (The Final Contradiction). But for now, these theories do not make predictions that can be tested, and so they’re not scientific in nature. They are simply mathematical extensions of quantum mechanics (Not Even Wrong). So right now we’re stuck. That being said, scientists area confident that they will make progress.

But What If…

Maybe the problem isn’t with general relativity and quantum mechanics, but with our fundamental understanding of reality. Maybe general relativity and quantum mechanics are both correct. As I said at the beginning of this article, a fundamental assumption that science makes is that reality is consistent. That is, a statement cannot be both true and false at the same time. But there’s a whole field of mathematics dedicated to logical frameworks in which it’s sometimes possible for a statement to be true and yet also false.

It’s called paraconsistent mathematics. And no, it’s not an area of mathematics where anything goes. In most cases, the logical framework does work just the same as our normal mathematics. It’s just that it’s a but looser. We allow, in certain instances, for contradiction, without “explosion” (normally if we have that a statement is indeed both true or false, then we can show that anything we wish is true, but not in paraconsistent systems).

Paraconsistent logic is one of my areas of interest, and I’m hoping that it can help solve another problem: the brittleness of Bayesian inference. It’s important to solve this brittleness, because it would allow us to actually turn science into a system which increases our confidence, even if still just provisionally, in the truth of a model of reality.

Right now, science is limited to falsification and therefore we only know when we’re likely to be wrong. There’s no justification for calling a theory true, or even likely to be true, simply because it’s succeeded in its testes. That’s known as “the problem of induction.” Bayesian inference seems to allow for this change, but in many cases we can end up with a situation where the results are actually a product of our initial guess, rather than on the chain of evidence.

But let’s say my work on paraconsistent yields results, and I can show that if we reformulate our theories in this paraconsistent framework, we can finally solve the problem of induction. That’s great! Now we can stop thinking of science purely as a way to know when we’re wrong, and we can start actually feeling confident that our well tested theories are true. It sounds like there are no downsides. Except that we could end up with cases where two contradictory theories could really both be correct. And that’s weird. Luckily, as far as I know, the apparent conflict with general relativity and quantum mechanics is about the only reason we have to maybe think that reality cannot be modeled in a consistent framework.

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The Narrative of Science

By alcanthro Leave a Comment Feb 10

This article is a full reply to a discussion with D. L. Shultz’s about his article on soft vs hard science (not fully safe for work).

So interesting! Science is indeed science, but I’m wondering whether everything that is scientific is necessarily science? I can take a scientific approach to understanding what I’ve written in my journal (how many words, which words, dates written, content analyses, etc.), but does this mean I’m doing science? I currently am leaning toward no, but am also loudly proclaiming that knowledge need neither be scientific nor based in the scientific method to be valid knowledge.

Before getting into science, let’s talk about mathematics. People often think that mathematics is all about numbers, counting, etc. But really, mathematics is the study of formal logic. It is the process of taking a collection of axioms and definitions, and seeing what we can produce with them, when we apply a system of logic.

Taking a number of axioms, including the ones necessary to formulate the natural number system and probability theory, and using the standard logical framework that we tend to use in mathematics, which requires that a proposition is either true or false, and never both, and combining it with the axiom that our most recent empirical observations are true, we’re able to construct mathematical models, which can potentially be falsified, by our empirical observations. That’s science.

Facts in Science

Evolution is fact is something I hear a lot. I understand why people might think that it is indeed fact, after all, it’s been tested over and over and over again, and all these tests seem to confirm evolutionary theory. But science is not about confirmation. It’s about falsification of theory, and if we give it a little bit of leeway, prioritization of theory. The reason why is because of the nature of science that I mentioned above. In science, we construct mathematical models, which can make predictions about observations we might make. Then we can use statistics to attempt to falsify the theory, in a form of statistical proof by contradiction.

But proof by contradiction does not allow us to determine that our assumption is correct. That’s because proof by contradiction starts off with the assumption that our position is true, and then we find a contradiction and so find that the position is actually false! That’s how hypothesis testing works as well. It starts by assuming that our hypothesis is true, and we work from there (The Basics of Hypothesis Tests).

And so?

So what does it mean to “do science?” It really just means constructing these mathematical models and testing them. But I would suggest a slightly more strict definition. Does testing a theory for the millionth time, just to learn how it’s done, in a classroom setting, mean that you’re doing science? Maybe? But if you’re developing new theory, or conducting a new test for existing theory, and are publishing your results in a scholarly format, whether in an academic paper, or preprint server, etc, for public scrutiny, then you’re doing science.

What about knowledge that isn’t scientific? Well, as I said, science is really just an extension of mathematics. So mathematics itself is very important. Our theories are, after all, just

mathematical models. Therefore mathematical knowledge is absolutely important, and yet it isn’t necessarily scientific.

A core maxim of mine is “Human subjects are not objects,” which means that humans are based in subjectivity, not objectivity, and that therefore any attempt to claim objective knowledge about human subjects requires objectification of the subjective. It is necessary and important that science eliminate subjective influence from inquiries about objects (e.g., atoms, chemicals, cells, etc.), but if the object of study are human subjects and/or subjective experiences (e.g., stress, emotion, belief, etc.), then the scientific method can only be used if the subjective is objectified, which is necessary but not sufficient for unethical treatment of humans…

I think part of this issue can be “resolved” by looking at science, and then looking at everything that is added to science. Science must be objective, as Shultz says. As I mentioned earlier, science generally boils down to very systematic and formulaic models. And really, science itself is just an extension of mathematics to the empirical world: we construct a model, compare it to observational data, and make a statement about probability from it (statistical proof by contradiction).

But then scientists do something else. We need to understand science in our own subjective way. So we take all our experimental data and results, and we construct that narrative. We take our radiometric dating, our results from smashing particles together, our comparisons to highly rigorous mathematical models like general relativity and quantum mechanics, and we construct a narrative.

In the grand scale, our narrative that we have constructed, and wrapped around our theories and data is as follows: roughly 13.8 billion years ago there was a massive explosion of energy, which quickly expanded. Over many years the results of this explosion cooled, and the energy coalesced into electrons, quarks, etc. Then eventually those coalesced into atoms, and then molecules, stars and planets…. well you get the idea.

Commenting on the original point of Shultz’ article, I suppose you could say that the “soft sciences” have a lot more narrative and more layers of narrative, but at the end of the day, the science itself is still just science. And there you have it. A lot more could be said on science, and I have said a lot more in my paper on reforming science. But I think this article covers enough to understand the basics of science, and also the narrative which is attached to it.

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Bill Nye, YHWH’s Pawn in Science Miscommunication?

By Daniel Goldman Leave a Comment Sep 23

For 25 years, Bill Nye the Pseudo-Science Guy has been miseducating people on the nature of science. Is he simply ignorant? Perhaps. Perhaps not.

Prologue: In order to help understand this discussion better, we suggest that our readers look through the following two papers on the philosophy of science and research methodology: “The Basics of Hypothesis Testing” and “Reforming Science.”

Bill Nye has benefited from a conflation between celebrity status and expertise. While people listen to him, because he is a celebrity, he has not earned his status, through extensive formal education or contribution to science. He has not published any scientific or philosophical papers, in either a peer reviewed journal or even any open publication database like OSF.

He has a bachelor’s in mechanical engineering. That is it. He is an inventor, and has contributed to exploration through his inventions, but this contribution would not grant him expert status, if not for him being a celebrity. And while science communication is very important, what he says is contrary to the very nature of scientific inquiry. He speaks of settled science, and various theories as if they were facts. He speaks of consensus among scientists, rather than consensus between theory and evidence.

Such positions take science and turn it into dogma, and he has been doing so for a quarter of a century. We need people who can promote the love of science. But why is his explanation of science so far off from the foundations of how it actually works? Is he simply ignorant of the philosophy of science and the mathematics behind it? Perhaps.

Or perhaps he is a pawn in YHWH’s evil plan. Creationists often reject evolution and other related theories, because they are just theories, and not proven fact. They compare theories of evolution to theories of gravity, thinking that gravity is proven true. It is not. No theory is proven true. All science is in a constant state of flux. But for that reason, it is never appropriate to reject a position, simply because it is not proven.

Bill Nye, and others like him, also have fought a war against religion. Their position on the topic is fairly unscientific in nature. But why would YHWH wish to fuel a war against religion? The answer could be “divide and conquer.” While we believe that YHWH wishes to be worshiped as a god, if he can keep people fighting with each other, it will make it easier to control them. The First Church of Penguinism seeks cooperation between science and religion, because it is through cooperation, rather than war, that we have the greatest chance of survival and prosperity.

And hopefully, over time, people will come to understand both science and religion, and we can peacefully coexist.

I realize that this discussion is probably not going to be well accepted, and is almost certainly not going to change anyone’s mind to the point of updating the NAS site. However, I decided I would still provide my two cents. For quite a while, I have had an issue with calling evolution fact. I am not saying that evolution is not true. I rely on the body of theory of evolution for my on research. However, that does not mean that we can know that evolution is true, or even likely to be true.

I am sure that you are well aware of the problem of induction. Hume and Popper are among many philosophers of science who recognized that it is difficult to justify the assumption that a theory is true, simply because it has shown to make accurate predictions in the past. We do assume that an unfalsified, yet repeatedly tested theory, is true. But we take this position as axiomatically true.

Bayesian inference seems to solve the problem of induction, but the issue with Bayesian inference is that it is brittle: it exhibits chaotic behavior with respect to the selection of priors. Falsification does not have this issue. That is because falsification is simply a statistical form of proof by contradiction, whether using the p-value approach or the Bayesian approach. We start by assuming that our theory is true, and that assumption gives us everything we need to take observations and estimate the probability of a theory being true. However, because we started with the assumption that the theory is true, stating that it is so is simply circular reasoning. Thus it is only valid to say that we have found a maximum probability of truth, or minimum probability of falsity, based on the observations recorded to date.

A theory becomes a fact, if it is certainly true, or at least very likely to be true. The brittleness of Bayesian inference, and the overall problem of induction allows us to make any claim about a theory being almost certainly true. Furthermore, without knowing how close to being true the body of theory on evolution actually is, we cannot say how likely it will be that it is falsified in the future. We cannot say anything about the distribution of future observations, except by assuming that which we are setting out to show, which again would be circular reasoning, except in the case of falsification.

I recognize the need to justify evolution, which is an incredibly robust body of theory, that is relied upon for so much of our research, our medicine, and so much more, especially when there is still a push to promote creationism as if it were valid theory, when it is not. But any attempt to defend evolution should not result in a misrepresentation of it as a theory or a misrepresentation of science itself. Perhaps one day the problem of induction will be solved, and when that occurs, it will be the most important advancement of scientific investigation itself, since the initial formalization of science. But today is not that day, and evolution cannot be taken as being anything more than a theory, which has yet to be falsified.

Thank you for your consideration,

Daniel Goldman

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The Fallacy of Scientific Consensus

By alcanthro Leave a Comment Sep 14

I have been involved in a number of arguments about scientific consensus. The most recent debate has convinced me to write about the topic in depth. The idea of scientific consensus has been popular since reports that 97% of all papers offering a position on climate change assert that climate change is happening. I am not going to address the validity of theories on climate change. But it is important to point out a number of issues with relying on consensus among scientists. First, peer reviewed publishing is dominated by a handful of authors.Consider the following statement from the abstract of “Estimates of the Continuously Publishing Core in the Scientific Workforce.”

Using the entire Scopus database, we estimated that there are 15,153,100 publishing scientists (distinct author identifiers) in the period 1996–2011. However, only 150,608 (<1%) of them have published something in each and every year in this 16-year period (uninterrupted, continuous presence [UCP] in the literature). This small core of scientists with UCP are far more cited than others, and they account for 41.7% of all papers in the same period and 87.1% of all papers with >1000 citations in the same period.

Basically, the work of roughly 1% of all publishing scientists account for 41.7% of papers published between 1996 and 2011. From this information alone, we know that any analysis of published articles is going to be skewed heavily towards the bias of 1% of the publishing community.

Aside from the “Academic 1%” there are a number of other biases that are expressed within the academic community. According to “Do Pressures to Publish Increase Scientists’ Bias? An Empirical Support from US States Data,” the “publish or perish” phenomenon, where academics are required to publish in order to keep their job, seems to result in a bias towards “positive results.” Studies which are inconclusive or are not consistent with the theory being addressed are thrown aside and focus is on papers that have “positive results.” This is doubly problematic as the goal in science is really to falsify a theory, rather than try to support it.

Neil deGrasse Tyson, in a tweet, stated that “anyone who thinks scientists like agreeing with one another has never attended a scientific conference.” This is fairly accurate. However, in at least some cases scientists also do not like expressing views which are very far from consensus. One of the most iconic examples of this situation was the feud between Newton and Hooke. While most people who have taken a science class are quite familiar with Sir Isaac Newton, fewer people are aware of the once prominent Hooke. Robert Hooke was the President of the Royal Society before Newton. Hooke viewed light as a wave. Newton viewed it as a particle. While light is now viewed as both wave and particle, this debate was problematic at the time. Newton actually waited until after Hooke died before publishing some of his work on the topic. That is how strong the fear of “retribution” for bucking the trend was.

Now, all of this together is not a falsification of the ability to measure robustness of a theory using scientific consensus. But it certainly is enough to question why people have so much faith in consensus. If I wanted to show that consensus was not a valid measure, I would need to actually provide statistically significant data. If someone else wanted to show that it was a valid measure, they would need to show evidence as well.

There is also a philosophical argument against the validity of consensus, even among experts. It has to do with the reason why appeal to authority is reasonable. Appeal to authority is often seen as a fallacy. But it is only a fallacy when the person is not reasonably considered an authority on a topic. For instance, if you argued that the Earth was flat because your parents told you that it was, that would be an appeal to false authority. If you argued that the Earth was round because a NASA astronaut who had been to space said it was round, that would be a reasonable appeal to authority.

But what makes appeal to authority valid at all? It has to do with expertise, or at least the assumption of expertise. When appeal to authority is used in a valid sense, a person is assumed to have full knowledge of the topic, and that they will honestly admit any gaps in the available information on the topic. Because of the assumed completeness of knowledge, an appeal to two authorities would have no additional information.

Even without this assumption, there are problems with relying on consensus. In response to my discussion, Jeremiah Traeger asked

Under a Bayesian prediction, if nine out of ten dentists tell you that you have a cavity, are you more or less likely to have a cavity? If nine out of ten doctors tell you that you have cancer, do you seek treatment? If a survey shows that 97 out of 100 actively publishing climate scientists state that global warming is occurring, what do you take from it? – A Tippling Philosopher

These questions are all interesting, but there is no single answer. The interesting point is that the author did not seem to care. The question alone seemed to act as some kind of justification in his mind. But to use Bayesian inference, we need to make a number of assumptions. We need to know something about how knowledge is distributed between individuals. Does each individual have knowledge that the other person does not? How much? Even if there is a difference, it may be so small that after a few experts are put together, there is almost no change in additional knowledge. So just referencing Bayesian inference, as if it somehow provides justification is a non starter. We need to know more information.

I find it disturbing how many people take scientific consensus at face value. The idea that we can measure the robustness of a theory based on how many scientists support it is interesting, but is not tested. And the position is itself a falsifiable statement and therefore, like all potential theories, should be tested before any claim on the topic is made. Until then, we have only one option: look at the data and the theory. See how the theory matches up with actual observations. This can be done by reviewing meta-analyses.

Pertussis

When I first wrote this piece, I did not include one example of consensus without evidence. I have been researching the efficacy of B. pertussis vaccines for some time. While there is a great deal of consensus on the efficacy of the vaccines, scientific data is not actually in line with this consensus. Studies that show efficacy conflate efficacy at preventing disease with efficacy at preventing infection. Multiple studies have found evidence against the view that the B. pertussis vaccines actually help prevent the spread of infection. Yet these studies are largely ignored by the medical community and there is no attempt to confirm them. Indeed, it seems like Pertussis could be nearly an epidemic but is going undetected because the majority of infections are asymptomatic. To see why, here is my analysis of a Chinese study, which could be conducted in the United States.

Update

This discussion has now been going back and forth for a while and I find it interesting to see the responses I get. One of the most interesting is seeing a classic shifting of burden of proof, from someone who is supposedly well versed in philosophical discussions. The following is from a rebuttal against what I have said.

To Quoque, my friend

One of the biggest criticisms against SA is that his own criticisms can be levelled directly back at him. He states things like:

“I’m still waiting for empirical data consistent with the assertion that the percentage of scientists that agree with a theory is a valid measure of the robustness of a theory.”

The thing is, I can reverse this:

I’m still waiting for empirical data consistent with the assertion that the percentage of scientists that agree with a theory is NOT a valid measure of the robustness of a theory.

He keeps ramming home this notion that we need an empirically evidenced piece of research to show that consensus is a good indicator of “robustness”, but fails to see that for the negation of this, he also needs to offer evidence. Because what is really happening here is pro-consensus is asserting something, and “anti”-consensus is asserting in rebuttal.

This falsification charge can also be levelled at his claim, too. You cannot confirm either claim, only falsify them. Which one gets falsified?

Now, Tippling statement would be reasonable if I actually said that scientific consensus was not a valid measure of the robustness of a theory. However, I never made such a claim. I responded to the claim that it was a reasonable measure, and demanded satisfaction of burden of proof. Therefore Tippling’s statement is just an attempt to make me defend my dismissal of an unevidenced statement.

Doctrine

Since initially writing this article, I have come across many other demands for relying on scientific consensus, and I have also come across another interesting problem. Academia has largely become a system of doctrine. The cult like nature of academia can be seen in answers on a Stack Exchange question and the response to my answer. The question posed was whether or not to publish a result which contradicts an existing mathematical result that has already been published. My answer is “yes.” The order in which academia received the results does not change the validity of either result. Only the result matters. If no error is obvious, after review, then both results should be thought of as being just as sound. To say otherwise (1) introduces doctrine and (2) suggests that somehow the probability of a result being correct is somehow determined by the order in which it was received. While such an idea is absurd, it is an idea that seems to be prominent and an idea which sways consensus: regardless of the validity of the result, any result which is inconsistent with consensus is placed under more scrutiny and therefore requires more evidence than it would if the result was published first and it became the accepted “truth.”