[00:00:04] Speaker A: ID the Future, a podcast about evolution and intelligent design to critically evaluate scientific claims. It helps to think like a scientist, but what are the good qualities of a scientist and how can we use them to evaluate the claims of Darwinism? Welcome to IDE of the Future. I'm your host Andrew McDermott and today I get to welcome Dr. Marci Reeves to the show to remind us how to think like a scientist and properly assess the claims of Neo Darwinism. Dr. Reeves is a fellow and Associate Research scientist at Discovery Institute's center for Science and Culture. She earned a PhD in cell and Molecular Biology with a Neuroscience concentration from the University of Hawaii. She also holds a BS in Animal Science from the University of Delaware.
She previously worked as a research scientist with Biologic Institute, investigating the origin of protein families and whether bacterial enzymes can be co opted to perform new functions. She has published her research in journals such as Cell Molecular Life Sciences, Biochemical Journal, Antioxidants and Redox Signaling, the Journal of Biological Complexity and Bio Complexity. Welcome to the podcast, Marcy.
[00:01:20] Speaker B: Thank you, Andrew. I am excited to be here and talk about science.
[00:01:26] Speaker A: Yeah, well, this is actually the first time I've interviewed you on ID the Future. So again, a hearty welcome. And I did want to first ask you where your interest in science came from and why you chose to study animal science and biology.
[00:01:41] Speaker B: I became interested in science or really am interested in science because I am a twin and that makes me genetically special. And so I think I've always had sort of an ingrained love for things that are science and things that are unique and learning about that kind of stuff.
And so yeah, I wanted to study science. I also love animals and so my original focus was in animal science. And then I, I decided to take. Take more of a research approach and I had an opportunity to do undergraduate research, which I think was really special because it opened my eyes to a lot of new things that I, I didn't even know you could do with genetics and molecular biology.
[00:02:26] Speaker A: Well, tell us how you first crossed paths with Discovery Institute and how you had the opportunity to work with Doug Axe and Ann Gager.
[00:02:34] Speaker B: Yes. So I first learned of Discovery Institute through the late Dr. Jonathan Wells.
We happen to attend the same church and we were introduced back in 2011 and I had recently completed my PhD at the University of Hawaii and relocated to the Seattle, Washington area.
And I was looking for a research position in a laboratory. And it was through Dr. Wells that I was introduced to Doug Axe, Dr. Doug Axe and Dr. Ann Gager, who had a laboratory biologic Institute in Seattle. And so it was through them I was able to get started working with Biologic Institute. And that eventually bridged the connection to Discovery Institute.
[00:03:22] Speaker A: Okay, and as we go through today's chat, perhaps you can talk about aspects of what you worked with them on and some of the results of that awesome research.
[00:03:33] Speaker B: Yes, absolutely.
[00:03:34] Speaker A: I was watching a presentation on YouTube you gave a few years back. It was called Scientifically Speaking Does Evolution Make Sense? And I thought it was an engaging talk you intended for all audiences so you don't have to have a specialized knowledge to enjoy it. And your goal, as you said, was to better equip people to critically evaluate scientific claims, and in particular the claims of neo Darwinism. Now, you emphasize the importance of thinking like a scientist, and I'd like to unpack just how you advise going about that so we can apply it to our own evaluation of competing scientific claims about the origin and development of life in the universe.
Now, by the way, can you start off by telling us why you think it's important to be armed with scientific arguments when we have conversations with people?
[00:04:22] Speaker B: Yes, absolutely.
I think that it is important to be able to follow evidence where it leads, rather than approaching variations in opinions. Because you will come across variation of opinion within the scientific community referred to as arguments. Often scientific consensus does not mean fact. It just tends to be the thing maybe we most are thinking about right now. And I think that within the scientific community, within the education systems, we need to be able to simply present what we know to be true without question, and also what we don't know.
I think that when you have differing of opinions, we might call it an argument, but we might be able to just say, hey, this is what I know and this is what I'm still trying to figure out. And this is maybe where shortcomings fall whenever I am speaking with young scientists in particular. But it doesn't matter whether they're young or whether they've been around a long time. But I always start by asking when the last time they took a biology class was. And a lot has changed depending on on the age of the person or the audience that I'm speaking with. And so I do always try to make it my goal to convey information in a way that no matter whether you have a science background or if your science is something different than biology, or you just have no background in it at all and you just are curious and want to learn. I'm a mother with three kids and I get questions all the time from my kids and their friends and Things like that. And so I have found that as I've talked to people, I want to make sure that they can learn and that they can hear what I have to say without belittling what they may not know. And what I mean by that is I remember having professors when I was learning say, trust me, I have a PhD, and this is the way that it is. And I just don't think saying trust the science is something that is really the way we should practice science. It's a phrase I think many of us might be especially tired of hearing since 2020, when we heard a lot of trust the science and as more evidence unfolded, some of what we were told to trust in the past isn't always what it was meant to be or what it was thought to be.
And so let me share another story, something a little bit different. But I remember when I was an undergrad, this was back in 2004, and I was new to the sciences, learning a little bit more about DNA, learning about the genetic code. And Craig Venter and his team came to the University of Delaware and spoke about how they had just completed the sequencing of the human genome. And I remember thinking in that, at that time, like, we've arrived as biologists and we, we must have all the answers because now we have this code. And it's just something that at the time was very exciting, very fascinating. It's, I'm not discrediting any of the work that they did, but the more that I have since learned over the years in molecular biology, the more I realized that they have this code and it's like a library full of books, but we don't necessarily have the translation for what all of those books mean and what all the letters and all the languages within that code are. And that's really the difference in the last, I mean, 20 plus years we're talking about since the sequencing of the human genome to where we are today. And so I think that's the part I try to convey to people, is when you're thinking like a scientist, you want to think, well, what did they know then? What do we know today?
Let's follow the evidence, where it leads. And, and that's really simply all you can ask.
[00:08:04] Speaker A: And it's, it's tempting, you know, for a specialist in a field to just say, look, trust me, okay? You, you know, I've done years of research. I have more specialized knowledge. But, you know, I, I'm an, I'm an educator as well, and I've, I've learned that you Know, just telling someone to trust you is not the best way to bring them in on the journey and allow them to learn.
In order for someone to trust something or someone, they have to feel safe and confident around it. And so, as you said, we've had plenty of opportunity of scientists who perhaps have been saying, hey, trust me in the last few years, but we don't have that. We don't feel safe to do so. And we don't have that confidence because we're not allowed to probe these important questions.
We're just told, you know, from on high. So that's a great point about really getting past that. Just trust me and, you know, allowing us to be able to have the freedom to explore these questions. Well, it's coming up for lunchtime, so let's talk about cake. Well, not really the cake that we can eat, of course, but you do use cake as an analogy when it comes to scientific data and perspectives on that data. If data is the bottom layer of the cake, what's often placed on top that we need to look out for?
[00:09:28] Speaker B: Yes, the icing. The icing on the cake, as it were. And so my, I love teaching with food analogies because I love to eat, but also I think that it, it very much is relatable to someone who doesn't entirely understand why are we talking about this? Why do we feel the need to break things down in such a way? It is so that anyone can follow this evidence and then you can draw your own conclusions and ask the right kind of questions. Right. So if we're looking at a cake that's been frosted and it's beautiful, the, the maker of that cake isn't going to be able to show us the, the raw cake underneath. And if there was a piece that broke off and they could kind of patch it back together with the icing. And so a lot of times, when you look at what is being put out there within the scientific community, what we see is a beautifully frosted cake. It's the story they wanted to tell. It's the kind of, the drawing of the conclusions that they came to. And we don't always get to see the raw, raw data, which would be like the cake underneath the icing. And so I think it's important to, if we really want to follow evidence where it leads, then we need to see the good, the bad and the ugly. Right, the unfrosted cake in this case.
[00:10:44] Speaker A: Yeah, that's very true.
We want to see those, those things that are underneath and pay attention to the raw Data, as you say, strip away those extra layers and understand them for what they are, you know.
[00:10:55] Speaker B: Yeah.
[00:10:56] Speaker A: Things to make it taste and look good.
Okay, well, let's define terms a little bit.
You say thinking like a scientist involves defining the terms that you're talking about. So why is that important to do, especially around major scientific questions?
[00:11:12] Speaker B: Yes, absolutely. I, I think it's very important to define what you're talking about. You may be talking with someone and you're on the same page, or they may come at it from a totally different perspective. So making sure that you guys all, everybody understands what we mean. So I like to think of something that tends to be more controversial, like evolution in three parts. And so when we say evolution, what do we mean? What are we talking about? So initially, right off the bat, it's change over time. Most people are not going to find that controversial. It's adaptation. It's Darwin's finches that he talked about in his publication on the Origin of Species, where the different beak shape of the finches was adapted to the different environment on the island. Right. So that's change over time, whether it was, you know, arid or tropical.
The second part is referred to as universal common descent, which really paints a picture. And I want to emphasize that it's painting a picture of life as a giant branching tree, AKA the tree of life.
This is different than a family tree, for example, where you've got, you know, your grandparents, mom, dad, parents, and then the kids. Right? That is actual data. And it's a family tree. And in this example, they're all humans. And so that is, in fact, data. Whereas this universal common descent branching tree is really painting the picture of common descent as a possibility of evolution.
And the last one, which is a little bit more complex, I'm going to call the mechanism of change. And that is taking natural selection, which by definition is a process where organisms that are better adapted to tend to survive and produce more offspring. And so if you take universal common descent and natural selection, that is what forms the core of Darwinian evolutionary theory and what we refer to today as neo Darwin is taking that evolutionary theory and combining it with our more current knowledge of DNA and genetics. And it's a claim that mutations in the DNA are providing the variation for which natural selection is then acting. And so I'll talk a little bit more about it when I talk about my research. But that, that's really the key kind of point that we wanted to ask. If this is possible, what does it look like if these DNA mutations happen and so I'll talk a little bit more about that.
But then the other thing I'd like to define is intelligent design. I look at intelligent design as just another scientific view where in certain features of the universe and of living things are best explained by an intelligent cause. And so I hope that that helps, that when I talk to people to define what I'm talking about in this way and look at evolution, so most people, adaptation, not a problem, common descent, you're going to have some varying perspectives on that. And then the mechanism of change. We'll talk about that when I talk about the research that I did.
[00:14:17] Speaker A: Very glad that you've broken down those terms for us and kind of demonstrated how to do that. It's so important to, to lay out the terms before you, you know, converse or debate with somebody.
[00:14:29] Speaker B: Yeah.
[00:14:29] Speaker A: Now it's important to stick to the facts as you're, you're saying, why should we pay attention to how scientific findings are conveyed? You use the example of the health benefits of red wine. Tell us about that.
[00:14:42] Speaker B: Yes, absolutely. So the research that I did as an undergraduate was heavily focused on nutrition and we were always looking at antioxidants. I've published in Antioxidant Redo Signaling Journal. And so this is a really fun one where it was conveyed on the radio, it was conveyed on all of the media outlets. And basically we were all told, hey, scientists are telling us that we should go out there and we should drink our red wine.
And the reason that I like that example is that when you actually look at the information and look at the data, that isn't really what the raw data was. So there's lots of studies on resveratrol, which is actually the antioxidant that is in red wine.
But the study that was done in rodents, if you scan scale up the amount of wine that they were giving to the rodents for humans, that would be equivalent to drinking six bottles of wine a day per day. So you might have a really healthy brain with your antioxidant, but your liver is going to be shot for sure. And so I think it's really important to look at the facts and make sure that if we're conveying information, whether it's from a scientist or even a media outlet, that that were accurate with it. And I think that most people really genuinely are trying to get it right. And maybe they were just excited because they like red wine and they thought, oh, now, cheers, I can have another glass.
But I think that we have so much information.
Lots and lots of technology today has allowed us to gather lots of facts and we need to make sure that we're giving those facts accurately. And I would say most media outlets don't do a great job of that. And that's why we put podcasts and things like this together, so that we share this information in an accurate way. And I genuinely think most scientists want the information out there accurately. I just think sometimes it's hard to really see that cake with all the frosting or to really tease out the evidence. And so that's why we're, we're doing what we're doing and trying to follow the evidence where it leads and ask those hard questions, even if maybe they, they don't line up with what we originally thought.
[00:16:56] Speaker A: Well, another characteristic of thinking like a scientist is remembering to distinguish correlation and causation. How can you help us with that?
[00:17:05] Speaker B: Yes. So I love, there's a graphic that I've, I've used in presentations and it states potatoes have skin, I have skin, therefore I'm a potato. And everybody always chuckles at that. But that's a perfect example of correlation is not causation in that case, because I am clearly not a potato. And so I think that we need to be sure that if we are drawing conclusions that we're using the hard evidence and not necessarily making conclusions that we just think might be there. And so let me give you another example. This is one that I, I, with my neuroscience background, I always found it kind of interesting. So did you know that in order to officially diagnose a patient with Alzheimer's disease, it can only be done post mortem, meaning after the person has passed away.
And then you would look at the brain on autopsy and what you would see in that Alzheimer's patient brain are the characteristic beta plaques that show up in the brain. And that then would be officially a diagnosis of Alzheimer's disease.
Oftentimes a patient that maybe they have a family history of it and they knew for certain there were plaques in the, the brain, they're going to draw the conclusion that a parent, a patient with dementia, might have it without necessarily doing the autopsy. But something very important to remember is that there are postmortem brains full of plaques and that patient does not have dementia or Alzheimer's disease.
So just because you have plaques doesn't mean you will have Alzheimer's, but you have to have plaques to be diagnosed with Alzheimer's. So that's a correlation causation that I think is important to distinguish another one, super simple. Someone who smokes other life might never get lung cancer. Someone who never smokes might get lung cancer. And so there's so much more going on. Lifestyle, genetics, they all play a role. And so I think that the conclusions that we draw based on the evidence that we have need to be very specific that based on these things, we're going to assume this. And note that that is the assumption.
Or based on this and this and this. It is this. There's a big difference between saying this is or we're assuming that. Does that make sense? That's the correlation.
[00:19:34] Speaker A: Yeah. And many of us have heard of that, but it's important to keep it in mind as we evaluate claims.
[00:19:41] Speaker B: Yeah.
[00:19:41] Speaker A: Now you say that thinking like a scientist means acknowledging that invention requires information.
I think of Bill Demski's book no Free Lunch. You can't get specified complexity, the type of information capable of generating truly novel biological complexity without intelligence.
Tell us why, every scientist, including us, Because I'll take this moment to remind our viewers and listeners that we are scientists, as Doug Axe has laid out in his book Undeniable. You know, from. From the moment we're born, we're looking around us, building conceptual models of the world around us and fine tuning them as we get new information.
I mean, that, in a nutshell, is what science is. And so we don't have specialized knowledge in a particular field to apply to those basic intuitions, but we can certainly function as scientists in our everyday world, and we have every right to ask these questions of especially of important scientific topics. So that's something to keep in mind. But do tell us why every scientist, including us, needs to remember that invention requires information.
[00:20:53] Speaker B: Yes, absolutely. Invention requires information. And I love this. I'm a mom, I said, of three kids, and I have successfully taught all three of my children how to tie their shoelaces.
And so when a young child sees a tied shoelace, they intuitively know that they must learn the knowledge of how to tie their laces from someone that has that knowledge. It's not just gonna poof, tie itself.
Now, they could maybe through trial and error, figure out a way to do it if given enough time.
But there is an intuitive thought there that we all have. That invention requires information. And if we want to think about science from the perspective of intelligent design, then we should think about things that are designed, things that are invented. Things like buildings and clothes and phones and the networks that make our smartphones work. So, you know, Alexander Graham Bell's original telephone is a far cry from the smartphones that we all carry around today.
But in Order for our smartphones to work, we have to have the network and the computer chips and all of the technology for the physical device, as well as the connection and the Internet and the sort of the software side of things.
But we also needed the original telephone to be there, and it didn't happen without the invention of all of those separate things coming together to give us our smartphones. And so I think if we think about things from an engineering perspective, it sometimes makes it a little bit easier for us to think, like from an intelligent design perspective. I think sometimes when we get to biology, because we can't see it, we can't see DNA so well. Right. Even when you're looking at a microscope at a cell, it is not the amorphous blob that early, early scientists thought it was. It's so complex and there's so much going on. And that's. I think that's a piece that people need to remember and take with them when they're trying to learn what's. What's going on.
[00:22:55] Speaker A: Yeah, definitely. Now, closely related to invention is the concept of probability. You say that accidental invention is improbable. I mean, think of origami cranes, for example. There's a piece of paper there. It's not going to, you know, turn into an origami crane unless an intelligent agent makes it. So that paper is just going to stay in the lowest state of equilibrium it can, which is the paper.
So it's very improbable that we would have accidental invention. We can know this from our own repeated observation and experience, too.
So how does this figure into the debate over Darwinian evolution?
[00:23:34] Speaker B: Yes, I love the example of thinking about an origami crane. And for anyone who has ever actually tried to fold and make an origami crane, you will know that there are many steps, and they are specific, specified, and ordered. And if you don't do them in that way, you will not fold your origami crane correctly. And so if you think about each of those steps as a probability or possibility of time to get to the end result, it there's so many ways you could mess it up, and only one way gets you to the correct origami crane. And so if we want to be thinking like a scientist, we would ask some questions. How do we make an origami crane? How many steps are there? Are they ordered or not? And we have to remember that we really can only test what we can observe. Right. And so we can observe, like a scientist, that origami cranes do not happen by Accident. And so then we could ask, okay, well, why don't they happen by accident? Is that because they're complicated? Is that because they're hard?
What are some of the reasons why this might not happen? And then keep in mind that complex living things require a similar type of information, just like folding the crane to be invented, to come to be. And so as a scientist, we can observe and conclude, we can draw a conclusion. For an origami crane to happen by accident would be very unlikely or improbable.
So we can do those same types of observation experiments. Experiments. When we think about the living, breathing, reproducing, like the real crane that flies in the world. Right. That is so much more complex and would require so many more steps.
So then we can draw that same conclusion that the fact that it got here by accident is very improbable.
[00:25:23] Speaker A: Yeah, we cannot take information for granted. And we need more biologists to realize this and understand that.
That there is no free lunch in the end, you know, someone's got to pay for it.
Well, you point out that we can only test what we can observe. How does this apply to the pursuit of scientific truth?
[00:25:42] Speaker B: As a molecular biologist, we want to generate a testable question and ask how likely and how probable do we see the arrival of new inventions, specifically inside the cell? And so this is actually what myself and other scientists set out to test in the laboratory. We asked, is it possible to get one protein to perform the function of a different protein? This would require mutating the DNA code or the instructions for that protein to be like another. So I love food examples. I already shared this. But if you think about a recipe as written, and you could make changes to that recipe, and you would get something different. So maybe you're going to start out with a cookie, a cookie recipe, and maybe you don't have butter, but you could substitute oil, and you'd still get a pretty good cookie. But if for some reason you substituted kale, you would probably not have a very good cookie. And so if the end goal is to get to the cookie, what kind of tweaking and changes could we make in inside of a protein and say, can it be the thing we're trying to get it to be? And so that's the questions that we asked inside the lab. And in the case of what we were looking at, the protein in question that we were aiming for the cell to make, this new protein was necessary for the cell to survive. And so if in fact, the cell evolved and the mutations happened in the way that lend towards survivability. Our culture would survive and grow, and if it didn't, it would die.
Does that make sense?
[00:27:19] Speaker A: Yeah, that does make sense, yeah. And that, that research that you were a part of, obviously very important in showing the limits of a Darwinian process to be able to create new proteins or change one to the other. So I'm glad you mentioned that. Now, you do talk about the difference between data, raw data, and an argument.
What is the difference? When does a scientific argument cease to be an argument?
[00:27:49] Speaker B: I think that it's important to look at what we know specifically and what we still are trying to figure out what we don't know. And here's another little comparison that I love.
So if I asked you, do you have a brain inside your head?
You would be like, well, yes, yes, I do. I sure hope I do.
But. But how do you know that you do? Have you looked inside your head?
Probably not.
And so when we look at evidence, there's a point where we want to gather as much of the picture as we can.
And the picture of the information that we have today is going to be so much more than scientists that came before us.
And I think that is really important.
That if the foundation of the evolutionary theory is from Darwin, that was 170 ish years ago that he published it, what do we have today? What evidence do we have today that says, okay, this is the data that we have today? And it really is conflicting with some of this previous work. Not to discredit the scientists that have come before and the work that they have done. They were working in a different environment than we have today. We have a lot more technology today.
And so when I was working in the laboratory and we were asking, okay, if we know all these life forms exist today, I, I just explained, we can look outside, we can see different animals, different life forms inside the cell.
As a molecular biologist, we know there are different proteins. Those proteins had to come about.
And so if we want to say, okay, how often do we see new proteins? And we set out to test it, we start with one. Can we make it be like another one? And that's what we did. And in the beginning of our experiment, we actually took two that were very similar in shape. I often like to think of them like a left hand and a right hand. Could you make this hand be like this hand?
And we designed our mutational changes at the DNA level to try to make one like the other. And when we, as smart as we are, we tried to design these changes, we could not make the new one perform like it should have, or what we had hypothesized it might be. And in fact, we. Our original intention was that we would arrive at a number of mutations and be able to say, okay, now we can calculate based on this exact number that it would take this many mutations and this many mutations would take this much time.
When we initially did it, we didn't arrive at a number when we designed our changes. So then we took a random approach.
So we asked, okay, if I want to get this enzyme to perform like this, enzyme A to B, and I randomly mutate it, can I make this happen? And then. And in this case, I did this experiment where instead of just starting with one, we actually had nine different starting points that were all very similar, but they did not perform the function of. It was called biof, the enzyme in question here. Could any of these nine perform the function of biof, which is vital for the cells to survive? And we did this in cells in living systems, which is very important because experiments like this have been done in a. I like to call it in a test tube, where you kind of make little chemistry experiments, experiments, and some things happen, but that's not in a living system, and that's very different.
So we randomly mutated the DNA and asked, okay, how many mutations? Is it possible?
We were not actually able to do it. We had hoped we would again so that we could arrive at a number. But we can calculate based on the number of mutations we tried and how long mutations, how long it would take to get that number of mutations, and it's just not possible. The number that we arrive at is astronomically huge. It's 10 to the 15 years in order to maybe have enough mutations to maybe get this new function, and that's just one new function.
So there's just vastly more wrong ways to arrange DNA than correct ways. And so there's not enough time for that to happen in order to convey the cell to survive during that time.
[00:32:25] Speaker A: It definitely drains the magic out of the Darwinian mechanism. It does, because you don't have unlimited time. You don't have unlimited creative power.
[00:32:33] Speaker B: Correct. Correct. And so that's. That's really. If we are asking the hard question of is evolution possible as we defined it, with this mechanism of change?
And we go out to test it in living systems, in the cell, on a variety of different starting points, and saying, can we make this happen?
And not only were we not able to make it happen, but with our calculations, it's older than the age of the universe. Would be the time it would take. And again, that's just for one maybe attempt.
So it's not possible. A statistician would tell you that's not possible.
And so it's very important to think in biology, in the sciences, something that is considered statistically significant is important to share. But there are trends that happen that aren't statistically significant. And in this case, this would be so significant that a mathematician or a statistician would say the chances are zero.
[00:33:36] Speaker A: Well, in looking at those studies you did with X and Gager, as well as the other work you've done in your career so far, what would you say are some of the biggest things you've come to realize about the claims of Darwinian evolution through that research and through the principles you've applied to yourself as a scientist?
[00:33:56] Speaker B: Yeah. So I think it's important we defined our terms. Let's revisit those. Okay, so we defined evolution in a few different ways. Change over time and common descent, which I emphasized, paints a picture and mechanism of change. And so the. The mechanism of change definition is that it's the mutations in the DNA that provide the variation that natural selection can now act upon. And when we set out to do that in a lab, it didn't happen.
And so if that doesn't happen, it begs the question, that tree of life, that painted picture, are we drawing a conclusion without actually looking at the data? Because what I see when I look at inside the cell or out and around is different.
Different proteins, different functions, information, lots of different life, and yet I don't see a line connecting them. I don't see it because we tried to do it and it didn't work.
So that's the kind of the icing on the cake. Okay, what's the actual cake? We know that the different protein forms exist. We know different life forms exist.
But where's the line that's drawing a conclusion that some earlier scientists hoped would be true, but right now, there's no actual line. I take away the lines.
And so the more I learn as a scientist, the more I realize how little we understand some of these connections. And what we once thought to be true is so not.
The evidence does not support it. And that's what we need to be able to talk about. We need to be able to say this was thought in this way based on this evidence. Now that we have this new evidence today, this technology, these publications.
So let's talk about it, let's share it so that the next generation of scientists, again, I'm a Mom, I want to be honest about what does the evidence say and what does it not say? And so I think that this really confirms that the evolutionary theory did not invent proteins and cell types and life forms. And it can only legitimately make that claim if we know that it can invent these things.
[00:36:16] Speaker A: Things.
[00:36:16] Speaker B: And so brings us back to our question of, you know, we, we can study the things that are here and the kind of survivability, adaptation, but not the arrival of the new things. And so if we look from the perspective of design, what can we, what can we learn? So is this new data confirming or revealing an argument to be true or false?
And so I think the scientific community at large really needs to be honest about where we are, and I think media outlets as well, I would hold them accountable to say you shouldn't make claims based on not really understanding it, which is why we're trying to get this information out there. Just because a scientist hopes their hypothesis is correct doesn't make it true.
I mean, we all want them to be correct. We hope we've done our due diligence and read all the information, but there is so much information out there and so much that we're still learning that we just have to make sure we say this is what we know and this is what we don't know.
[00:37:20] Speaker A: Yeah. And you, I think you used the phrase due diligence. You know, that's a very important thing. As, as the person receiving a scientific piece of information, we've got to do that due diligence and verify it, you know, and look into it, get a second or third opinion, you know, before we trust it. You know, I think that's very important.
Well, what's the best way for our audience to follow your work and learn more about the research you've done?
[00:37:50] Speaker B: Well, all of my research is open access. Everything that I've ever published in open access journals, which I'm a big proponent of, little things in the scientific community that have always kind of bugged me are that not everything is open access. And I get it, they have a job to do.
But making the next generation of scientists pay to have to read what you did, I don't think that's how it should be. I think that open access, all the information that you have, should be out there. I remember even in my early publishing days wanting to put information in papers and saying, well, we did this experiment, why not include it? And a lot of times it just felt like, oh, this is too much of a story to tell. People won't be able to follow it if you add in too much information.
And so I, I would rather include it all and let people be able to decide so at least that they have it. But yeah, everything is published in open access. My, my real name is Mary Claire Reeves. And so you can actually just look me up. It's somewhat unique from Marcy, but everybody calls me Marcy, so.
But you can look me up and find my publications that way.
[00:39:04] Speaker A: Yeah. And how is Mary Claire spelled?
[00:39:07] Speaker B: M A R I C L A I R. So Mary with an I. One word Claire.
Yeah.
[00:39:15] Speaker A: Cool.
Yeah. Well, I've enjoyed this chat. You know, I think it's, I think it's great when we can look at how we can function as a scientist and think like a scientist.
It's been a stimulating exercise to remind us how to do that as we critically evaluate scientific claims. So I appreciate your time, Marcy, today, and I hope you can come back and unpack it some more in a future episode.
[00:39:40] Speaker B: Yes, absolutely. Thank you, Andrew, for having me.
[00:39:42] Speaker A: Well, to our audience, don't forget to subscribe to the new ID the Future channel on YouTube so you can be the first to know about new video interviews, commentaries and more. Just like this one will be available there too. So help us get the new channel going. You can
[email protected] dthefuture YouTube.com d the future well, thank you very much for joining us. I'm Andrew McDermott. For ID the Future, visit
[email protected] and intelligent design.org this program is Copyright Discovery Institute and recorded by its center for Science and Culture.
