#1245 Jacob Stegenga - Heart of Science: A Philosophy of Scientific Inquiry

00:00 Ricardo Lopes: Hello everyone. Welcome to a new episode of The Dissenter. I'm your host, as always, Richard Lobst, and today I'm here with a return guest, Dr. Jacob Steenge. He's now a professor of philosophy at Nanyang Technological University in Singapore, and today we're going to talk about his latest book Heart of Science A Philosophy of Scientific Inquiry. So Dr. Steengo, welcome back to the show. It's always always a pleasure to everyone.

00:28 Jacob Stegenga: Thanks Ricardo, thanks, and uh it's nice to be here and I should say you can call me Jacob.

00:33 Ricardo Lopes: Oh, OK, OK, I will try, I will try. Uh, SO, I, I mean, I, I know this, that this is a very broad question and some philosophers of science actually spent almost their entire careers just focusing on it and there's entire books written about it, but uh I would like, I, I, I think it's just a good. Question to start on, so what is science and what is scientific inquiry?

01:04 Jacob Stegenga: Yeah, OK, so thanks for that question. I mean, as you note, it's a really broad question and the subtitle of my book is a philosophy of scientific inquiry. So it really would take a whole book or a whole library to properly answer this question. But, um, even if I give a short answer, you know, it might raise further things for us to talk about. Um, SO, so, you know, science, science, of course, what science is trying to do is to learn about the world. Uh, SCIENCE is trying to discover facts about, about nature, facts about the world. Um, SCIENCE as an activity involves both, typically it's an easy way to Categorize scientific work is into empirical work and theoretical work, so a lot of science is about the gathering of evidence, and then a big part of science is about how to make sense of that evidence theoretically, and those theoretical claims are supposed to be, you know, claims about the world, claims that we can come to understand about the world. So there's empirical work, theoretical work in an attempt to learn about the world.

02:09 Ricardo Lopes: And what is the aim of science, or if not, what the aim is, what should it be?

02:20 Jacob Stegenga: Thanks, yeah, yeah. OK, so this is a long standing question for philosophers of science. Philosophers of science have offered many different answers to this question. Uh, I take a chapter in the book to give an account of what I take the aim of science to be. Just as a preface to answering this question though, when we're speaking about the aim of science. We need to be clear that we're speaking about what I, what I call the constitutive aim of science. So what I mean is we're not speaking about the kind of sum total of all of the particular aims of the particular stakeholders in science, like the aims of scientists, the aims of science students, the aims of science funders. People have different aims, right? Some scientists want to become rich and famous. Some want to discover facts about cancer and so on. So we don't mean the particular aims of particular scientists, but rather, um, when we speak about the aims of science, we're speaking about the constitutive aim of science as an institution. So think about like, The game of chess. The game of chess has a constitutive aim that is victory, and it doesn't really matter if like when you play chess with your nephew, you're doing it just to teach him how to play chess or just to have fun or spend time with your nephew. You have various aims, right? The constitutive aim of chess is victory, um, regardless of the particular aims of particular agents in that endeavor. So I argue that the constitutive aim of science is, is what I call common knowledge. So it's a special kind of knowledge. So common knowledge is um a justified true claim about the world, that's the knowledge part in which there's both consensus about the claim, broad consensus and consensus about the justifications that are on offer for that claim. So it's not to be confused with something like common sense, right? So common sense is not what I'm after here. Common knowledge is a, is a kind of technical notion, uh, which involves, um, two components broad consensus about a claim and broad consensus about the justification for that claim. So, so that's the aim of the constitutive aim of science, and I argue that. Science at its best routinely achieves that aim.

04:45 Ricardo Lopes: Right. I, I mean, you mentioned, uh, three different things there and we have to go through each of them. You mentioned common knowledge, scientific consensus, and justification, but, uh, uh, just before we get into that, uh, I would like to ask you because I've had conversations on the show. With, for example, sociologists of science and philosophers of science, and this is something that is brought up very frequently. What do you make of when, for example, certain scientists claim that The aim of science is to uncover objective truth. I mean, is that really the aim of science, or should it be, I mean, of course it depends on what you mean by objective truth, right? But since science. IS an institution, a social institution like any other, which operates in a particular socio-cultural context. And later, I guess when we talk about whether science can be value-free at all, if that's possible, we'll come back to some of what we're going to discuss here. But is it really something that scientists should aim at objective truth, and is that even, uh, like possible taking into account that science is a human endeavor and that, as I said, it operates under a certain specific, uh, under a specific social con uh cultural context.

06:21 Jacob Stegenga: Yeah, so it's a really good question and there's a lot packed in there. So, you know, you mentioned some sociologists of science and some philosophers of science who have, at least historically, Often doubted that science can attain a sort of special objective truth, a sort of view from nowhere. Um, THERE'S a lot of arguments that have been on offer for that kind of view, that sort of skeptical view about science. Now I think that those sorts of views are often radically exaggerated, um, and in fact, this, they are more and more falling out of favor too. So, so the short, my short statement here, my, my kind of programmatic statement is that um, Science at its best can achieve objective truth, and scientists ought to be aiming at that. Um, SO there are very simple examples like, you know, the structure of DNA as a double helix or dinosaurs existed, um, you know, these are claims that are objectively true. They, they'll be true for all time. There's broad consensus about these claims to deny these claims, to say like the structure of double of DNA is a triple helix, say you'd have to be, you know, just completely disconnected from um actual science. So science, science achieves common knowledge, um, I in the book I also call these timeless truths. These are objective, objective claims about the world. Now I think that um. There's a lot of things though, there's a lot of nuance to add to this though. What we see in science often is a kind of overconfidence in. In among scientists making particular claims to particular putative truths that turn out not to be true, and so as we know, you know, scientists are often quite, quite overconfident in their putative claims to truth, and it's a virtue among all people, including scientists, to be humble, and so. Um, HUMILITY, of course, is a virtue. Scientists should be humble, but I don't think that they should be so humble such that they would, they would hold that, you know, science can never achieve truths. Science does achieve objective truths like DNA is a double helix. Um, SO, OK, there's the humility point, um, and of course there's another sort of important argument that many sociologists and philosophers of science make, which is that, um, We have this long history of scientific theories being held up as true and then later coming to be seen as false. And so one, you know, one way this is sometimes put sort of colorfully is that science is a like a graveyard of dead theories. All the theories in the past have been shown to be false and therefore our theories today are very likely to be false. That's a, that's a common argument. Philosophers sometimes call this the pessimistic meta induction. Now in the book I argue against this pessimistic meta-induction. Um, THERE, there are, there are really compelling reasons to not be too swayed by, by that worry. Of course, again, it's good to be humble, but I think it's also good to recognize real scientific achievements when they exist and, and, and, and we do have them. Science at its best has, has really great achievements, um. Another angle to this is in that in that overconfidence point. Um, I think some areas of science at some periods in history have been more successful at achieving the aim of common knowledge than other areas of science at other times. Um, SO for example, like physics of the early 20th century was just wildly successful at discovering like really important facts about, uh, about the universe, about the structure of the atom, and so on. Um, PHYSICS today. IS much more in a sense theoretical and much less tethered to um Um Novel empirical methods of testing contemporary theories, and so in short, the theories are are departing more and more from. The kind of evidence that we can gather to test them and so in, in like physics at its cutting edge should always be humble, um, and so. Um, IT'S one thing to say, yeah, science at its best can achieve common knowledge, particularly like the science at its best, say, physics of the early 20th century, but, but we should also maintain a kind of humility in, um, in thinking about science, especially science at its cutting edge, um, and there's, there's, you know, lots and lots of examples of this. Yeah.

11:14 Ricardo Lopes: So let's get into the particular concepts that you mentioned in your previous answers and sort of, uh, tackle them one at a time. So what is, what do you mean by common knowledge and how does it manifest in the domain of science?

11:34 Jacob Stegenga: OK, yeah, so, so abstractly what I mean by common knowledge is, um, a true claim that is. Um, VERY well justified by a relevant scientific community. That scientific community has broad consensus about the claim and broad consensus about the justification for that claim. Um, SO that's, that's common knowledge. There's lots of examples of this, like, you know, DNA is a double helix, that's, um, that's a very simple one, or, you know, the sun is at the center of the solar system, it's another simple one, Now That abstract definition raises some secondary questions like what's the relevant community, what proportion of the relevant community has to assent to the claim, what proportion of the relevant community has to assent to the justifications for that claim. Um, I work out some of these nuances in the book, so I think the relevant community is essentially the community of, of relevant experts. So if it's a claim about The structure of DNA, then it's, you know, biochemists, for example,

12:39 Ricardo Lopes: and genetic geneticists in that case as well.

12:43 Jacob Stegenga: Exactly, yeah, yeah. Now there will be like grays, there will be particular individuals for whom there's a kind of gray zone status about their, you know, um, membership in this community of experts. So like a biochemist who got half a PhD and then dropped out, right? Well, Have a lot of expertise or like say a physicist who happens to like know a lot about biochemistry because he's, you know, consulting on the use of particular instruments in this biochemistry lab, so he learns a lot. So he's an expert in one area, but he also learns a lot about this this related area. So there are gray zone cases, but you know, that's life. So there's like a set of experts with some gray zone cases and then we can imagine within that set of experts sort of two. A two dimensional. Um, TWO dimensional graph, for example, and one of the dimensions is the proportion of members of that community that assents to a claim. And the other dimension is the proportion of members of the community that assent to the justification or the various justifications for that claim. We have common knowledge when we have high values on those two dimensions. So when we're sort of far in the corner of this two dimensional graph, um, so that that's common knowledge.

13:57 Ricardo Lopes: Yeah, no, that's really interesting because I mean, I've been doing the show for 8 years now. I've interviewed more than 1200 people and it's interesting that over time, Uh, I've become more and more attuned to, I mean, what, uh, what's the level of expertise of each person when it comes to each subject and the ones I should take more or less seriously in terms of their claims, because, for example, I can even Ask a paleoanthropologist about the evolution of Homo erectus, for example, or the colonization or the migration of humans to the Americas, but I mean he or she might not be specialized in that. A specific domain of paleoanthropology. Perhaps I have to ask directly someone who actually works on the migration of Homo sapiens in the Americas or on the evolution of Homo erectus, uh, so that's um. Higher level of resolution, I guess in terms of specialization. It's not that the paleoanthropologist is not enough of an expert on the topic perhaps, but uh some people who actually work specifically on that subject should be taken more seriously,

15:25 Jacob Stegenga: I

15:25 Ricardo Lopes: guess.

15:26 Jacob Stegenga: You know, that's a really interesting point. Yeah, um, that's, by the way, congrats on your show. I, I, I, I remember appearing on your show six years ago to talk about my first book, Medical nihilism. That was a great interview. Um, AND your show has really developed since then. Uh, CONGRATS. Thank you. Thank you. That, that, that, that feature that you were just mentioning though, that's an interesting idea that I, that I didn't think about in the book, but this is worth, worth thinking through maybe in future work, which is that. Perhaps the relevant scientific community, um, in fact, or the relevant community for assessing whether or not some claim is common knowledge can expand over time when the claim, um, becomes just better and better and better justified. So for example, I would say that the relevant community for assessing whether or not the sun is at the center of the solar system is a really broad community of basically educated. People generally, right, um, where, whereas the relevant community to assess some cutting edge feature of, yeah, you know, uh, archaeology, for instance, would be a much narrower,

16:24 Ricardo Lopes: yeah, mhm, yeah, uh, but you mentioned consensus, so, uh, how do we achieve consensus in science?

16:33 Jacob Stegenga: Right, so I mean, obviously what consensus is, is essentially broad agreement among a relevant community about some claim, um, how

16:41 Ricardo Lopes: we but but broad agreement, I mean, does it have to correspond to a particular percentage of the uh the relevant experts agreeing with a particular claim or does the percentage not, Uh, not matter that much.

16:59 Jacob Stegenga: Uh, I mean, I think it's going to be really high, so I think, um, for realistic cases of consensus, like the examples we've been using, dinosaurs existed, for example, um, the relevant percentage is going to be extremely, extremely high, um. But you know if there's, if there's 2 or 3% dissenters on a in a relevant, uh, for a relevant claim in a relevant scientific community, that's, you know, high enough anyways, 97% of members of the community assent, that's really good. Um, IS it sufficient to count as common knowledge? I mean, again, I think they're like it's, it's a. The, the proportion of members of a community that assent to a claim is obviously a gradable property, right? Um, SAME with the proportion of members of a community that, um, assent to the justification for the claim. And so when I, when we have that visual two dimensional graph way of thinking about the, uh, definition of common knowledge. It's, it's common knowledge is claims in the top corner of that, of that graph, but because these are gradable properties, there will be these gray zone cases, 97% might be high enough, but maybe 94% won't, um. So these are line drawing problems and line drawing. I'm not too interested in line drawing problems, um, yeah, so then, but your question was how do we get, um, consensus, yeah, and you know there's a variety, variety of ways we, we can get consensus. We can get consensus by, um, genuine epistemic means. So what I mean here is like genuine. Strategies for justification and this is what I argue is like crucial for science. So, um, scientists make a claim, they publicly articulate reasons for that claim. They in so doing, they expose those reasons to criticism, um, so they expose their reasons to criticism, and that allows them to improve upon those reasons, respond to the criticisms, and thereby improve the quality of their, of their reasons for that claim. And that, and that of course occurs in a very kind of networked, you know, like sciences, scientific communities are very networked, they're very social. So multiple teams will be doing this, multiple teams will be generating reasons and generating criticisms for the reasons, and that, that generates a very like, you know, robust kind of justification for scientific claims. There are other ways we can get consensus, so we can get consensus, for example, by propaganda or by bullying, um, and those are of course epistemically inappropriate ways to get consensus. If you, if we try to get consensus by bullying or by propaganda, um, we're doing so in a way that doesn't provide genuine justification for the claims at stake. So the kind of consensus that that science um needs and routinely gets is consensus that's based on um genuine modes of justification in this sort of public sphere. Mhm.

20:02 Ricardo Lopes: Uh, AND, uh, another concept I would like to clarify here is the one of justification. You also use that term and it is a very important aspect of your book and we're going to get more into that. But what do you mean by justification and why do you focus so much on it in your book?

20:22 Jacob Stegenga: Yeah, thanks. So. Right, I'm arguing that the kind of slogan of the book is, is that the heart of science is justification. Um, SO justification is this crucial notion in the book, um. We can understand justification, however, in relatively non-technical ways. Justification in science is essentially the provision of reasons for some claim. So, um, you know, you've got, when you make a prediction about what the weather is going to be like in Portugal tomorrow, um, you're doing it on the basis of some reasons. Some of those reasons are better, some are worse, um, and if those reasons are indeed, you know, uh, they're, they're justificatory if they provide good grounds for your prediction about the weather. Science, of course, like scientific practice is, is just shot through with the provision of putative justifications for various claims. So you provide evidence for some, for some theory, uh, you provide a logical argument or mathematical argument buttressed by some empirical evidence, um, that gets you justification, um. So we can think about justification as essentially like an argument in the philosopher's sense, namely the provision of reasons, empirical, mathematical, theoretical for some claim about the way the world is.

21:49 Ricardo Lopes: Uh, AND, uh, in the book, uh, I would like to ask you about this because it's a very interesting, uh, claim. Uh, YOU, you claim that scientific testimony should not be based on whether the scientific work and the evaluation has achieved its aim, but rather on justifying scientific claims. Could, could you explain that?

22:12 Jacob Stegenga: Yes, good, yeah. So actually there's a um so what you just said is is right about one of the chapters, but that general principle is something that I'm applying to um many different ideas in science. So the general idea is, um, although I argue that the constitutive aim of science is common knowledge, I go on further to argue that for any evaluative. Principle about science, the, the principle itself should not be based on whether or not some scientific work achieves the aim, but rather it should be based on the quality of justification for the claims being made. So in other words, Good science in whatever kind of goodness we're assessing, good science should justify its claims, but it doesn't have to attain its aims. That's the slogan. Um, SO if we're speaking about good scientific testimony, for example, like some scientist makes some claim about the world to a scientific journal or to a policymaker or in a public relations campaign, in a, you know, a press conference or writes an article for The New York Times, whenever a scientist is making a claim about the world. Our evaluation of that claim should just always be based on the extent to which that claim is justified. Now there are more nuances than this, but that that's, that's the core idea. So It should not be based on whether or not the claim is true, and there's a bunch of reasons for this, but the main idea is that, you know, truth in science is determined typically retrospectively, so it took, for example, many decades for us to finally come to accept the Copernican theory of the solar system. So truth is decided retrospectively, but we want modes of assessing science for science in real time. So our principles for assessing good science, good scientific testimony, um, bad science is this pseudoscience. We want to be able to assess. Real-time science, um, and real-time science is accessible. According to the extent to which that science is justified, that scientific work is justified, rather than whether or not it's true.

24:46 Ricardo Lopes: Mhm. And of course, we are talking about epistemology here, but would you also, in this, in this case when it comes to the aim of science, because, uh, I mean, we live in a particular, uh, economic system, in a particular sociopolitical system that, uh, I mean, for, for example, most of the time for scientists to be able to properly conduct their work, they have to have. Access to particular funds and they have to justify why the research they're going to conduct is necessary and many times that is justified in economic terms because it will eventually produce certain economic gains and so on. I mean when it comes to scientific, uh, the production of scientific knowledge. And focusing exclusively on epistemology, uh, that is, that shouldn't even be relevant, right? Whether, uh, a partic whether a particular piece of knowledge has some, uh, economic or produces economic benefits or not, right? I mean that that's not particularly uh relevant here.

26:10 Jacob Stegenga: Yeah, good. So that's, this is a really important question. And my own view is that the answer to your question is yes, right. It's, it's, it's, it's not relevant when we're speaking about the constitutive aim of science. Yeah. Now, of course, it's true that um science often leads to. Um, MATERIAL benefits, practical benefits, technologies, um, that, that's of course true, and of course that those benefits can be a reason to do science, um, that's of course true, uh, and insofar as, you know, taxpayers are supporting, uh, publicly funded scientists, then taxpayers can expect that the kind of science that they're supporting will at least sometimes have in the long run. Practical payoffs for the society that's um funding that scientific work. So, so that's all true, um. But when we're speaking about, you know, science as, as an institution as such, um. We can conceive of the constitutive aim of science in these purely epistemic terms rather than practical terms, yeah.

27:24 Ricardo Lopes: Yeah, yeah, but no, I just wanted to ask you that because, you know, nowadays because of the economic system we live in, uh, sometimes and because particularly, I mean this is more of a personal thing, but I just love knowledge for knowledge's sake. I mean, for me it's many times it's a bit frustrating how people want to or try to. Reduce all scientific knowledge or all the relevance of science and even philosophy to whether it produces some form of economic value or not, and I, I, I think that's not the way we should approach epistemology and the production of knowledge, whether it is scientific or philosophical or any other sort of knowledge, so.

28:19 Jacob Stegenga: Yeah, I think that's exactly right. So, um, you know, what one of our well-known colleagues in philosophy of science, a very, a very eminent colleague, uh, Philip Kitcher has this argument that, um, science should be, uh, aiming to discover what he calls significant truths, and for him significance has, Two basic basic properties or you get significance through um sort of 22 possibilities. One is if the scientific work can have practical payoffs, but the other is if it can sort of satisfy a sort of basic intellectual curiosity. Um, NOW there are various constraints on that. So for example, you could imagine a scientist who has this sort of like perverse goal of counting the number of grains of sand on a particular beach, right? So he's just curious about that. It's clearly not going to have any payoff for anybody, um, but that that particular scientist is, is just curious about that. Um, PRESUMABLY we'd want to say that, um, That curiosity is misdirected. It's certainly not worth supporting through any, you know, government funded research dollars. Um, SO, so there's got to be constraints on the kinds of curiosity that, that are sufficient to really motivate a scientific project and the support of that scientific project. Typically some candidate constraints are basically like broad. Theoretical knowledge that unifies or or or makes more coherent our our broad set of scientific claims about the world, so you know it doesn't really matter how many grains of sand there are on this particular beach, knowing that it's not going to help us sort of figure out anything else about the world, yeah.

30:01 Ricardo Lopes: Yeah, yeah, I, I, I mean, but, uh, that, that's perhaps, uh, that, that example of counting the grains of sand in a beach. I mean, that's, uh, uh, uh, an, an exaggerated example because I mean, I don't think that. Anyone would, would, would really be, uh, genuinely interested in that. I mean, I, I was just trying to make the point that many times even when it comes to very abstract things like, for example, the theoretical foundations of a particular scientific field. I mean, sometimes that's a, a really necessary work, but the economic Payoff is not obvious at all, and I, I mean it's still work that needs to be done if we are to conduct ourselves in a proper scientific way, I guess.

30:52 Jacob Stegenga: That's absolutely right, yeah, and you know, we are curious kinds of creatures, right? We want to understand ourselves, not because that's going to make us richer or or live longer, right, but we're just genuinely curious. Agents that want to understand where we've come from, uh, what's out there in the world, what's out there in the, in the, you know, the stars above, um, how to be a good person, where do we get meaning in life, you know, there's all these like really deep questions that people are genuinely curious about, um, regardless of any kind of practical payoffs, yeah, yeah.

31:27 Ricardo Lopes: Uh, SO, uh, in the book and going back to what we were talking about in terms of, uh, justification and, uh, what, uh, and the aims of science and all of that, uh, in the book you suggested deontic rather than a consequentialist evaluation of science. So what do you mean by that exactly?

31:48 Jacob Stegenga: Yeah, yeah, thanks. So these are, these are philosophical terms of art, deontic and consequentialist. And these terms, I'm, I'm using to get at that idea that we, that we were getting at a few minutes ago, um, that is. Evaluation of science in any kind of mode of evaluation should be focused on the extent to which Scientific work satisfies various justificatory principles rather than the extent to which science achieves its aim. So, so that's the general idea. Yeah. So I, I take that distinction between deontic or deontological. Sometimes, sometimes you hear that term and consequentialist. Those are terms of art from ethics. So a deontic ethics is an ethics that's focused on, um. Proper right behavior that follows principles rather and whereas consequentialist ethics focuses on, um, you know, um. You, you, it, it basically is the idea that the, the right act in any decision is the act that you know maximizes good consequences, even if maximizing good consequences violates certain principles. So that's the distinction and I just apply that distinction to judging science.

33:11 Ricardo Lopes: Yeah, but, but in this case, you, it's in epistemological terms, right, you're using theontic and consequentialist. Uh, WITHIN the domain of epistemology and so it's not a moral or ethical evaluation of science,

33:26 Jacob Stegenga: right, right,

33:29 Ricardo Lopes: yeah, uh, and what do you make of factive approaches to science? I mean, first of all, what is effective approach and then, uh, what do you make of it?

33:39 Jacob Stegenga: OK, right, so this question is getting into some. Is a a pretty fine grained. Uh, ISSUE for epistemologists, um, so. Factive approaches to epistemology broadly say that um our evaluative stance for some domain should be based on. Whether or not Some agent in that domain is getting at the truth or speaking the truth. Um, THAT'S what factive means. So a factive claim about testimony, like a factive view about testimony, says that appropriate, like testimony is appropriate only if the testimony is true. Um, SO that's what the term factive means. For epistemologists, yeah, and now are you asking me to, so I mean I can go on here, so I, um,

34:44 Ricardo Lopes: yeah, because the other part of my question was what do you make of effective approach to science,

34:53 Jacob Stegenga: right, yeah, exactly, OK, so. For any feature of science that we would be thinking about from a philosophical point of view, like for example, scientific progress, what is scientific progress or appropriate scientific testimony, when is scientific testimony appropriate, when is it inappropriate, um. We can take a factive approach to those questions, so a factive. POSITION on scientific progress would say science makes progress when it accumulates facts, when it accumulates true claims about the world, so that's a fact of Theory about scientific progress or a factive theory about scientific assertion says or testimony, scientific testimony is appropriate only if that testimony asserts facts, asserts truths. So those are factive views about science and they're very popular among epistemologists. And philosophers of science. So the most, the, the most common theories of scientific progress among philosophers are factive, and the most common theories about testimony among epistemologists are factive. Now, I spent a chapter in the book arguing against these um epistemologies. I, I, I spent a chapter arguing that factive approaches to scientific evaluation. ARE inappropriate. Um, WE should have, we should be non. Factivists, we should have non-factive, um, evaluative. Norms or principles, um, what do I mean by nonfactive? I mean, Evaluative principles that are based on the extent to which scientific work is justified rather than um the extent to which that science gets at the at the truth, that's that's the general idea.

36:51 Ricardo Lopes: And you mentioned scientific testimony. Which features do you think good scientific testimony should have then?

37:01 Jacob Stegenga: OK, yeah, OK, thanks. So, I think Assertion in general and especially scientific assertion or scientific testimony. There are 2 good making features. Of scientific testimony, and I think in testimony in general. One is that the testimony be justified, uh, so if somebody tells you that, um, the weather in Lisbon tomorrow is going to be sunny, you want that claim to be justified, right? They're making a claim about the way the world is. You want them to have good reasons for that claim, you don't want them to just be making it up, lying, or making it, or maybe they had. Uh, SOME weather report from a notoriously unreliable weather forecaster, right? You want them to have good reasons, reliable reasons for what they're saying. That's like the good making feature number 1. Good making feature number 2 is that the, Information that's being asserted be truly informative. Now notice that. Um, The degree of justification of a claim and the degree of informativeness of a claim can trade off against each other because We can artificially make. Testimony more justified by making it vaguer and vaguer and basically hedging our claims. So for example, if you ask your friend what the weather is going to be like in Lisbon tomorrow and they say, um, they could say something like, it's either going to be rainy or it's not going to be rainy. I mean that's like logically true, right, so it's like maximally justified, but it's not informative, it's not going to help you decide whether or not you should carry the umbrella, right? Or if they say to you, it's going to be greater than 0 °C, right, that's really well justified as a prediction about what the weather in Lisbon's going to be like in April. But it's not informative at all, right? Because it doesn't tell you like should you carry a sweater or an umbrella or something like that. Um, SO there, there are these two properties, justification and informativeness about assertion in general and scientific assertion in particular. They can trade off against each other, and what we want is scientific testimony that tries to maximize both. And I, I give An argument in the book that justification has some degree of priority over informativeness. So if we have to give up one, we can give up informativeness, but we Always want our scientific testimony to be um well justified. Mhm.

39:40 Ricardo Lopes: So I would like to ask you a little bit about the demarcation problem. This is uh something that in the philosophy of science, a topic in the philosophy of science that I've explored on the shows in several different uh interviews, but, uh, what do you make of it? I mean, how should we address the problem of Trying to demarcate science from pseudoscience or and or anti-science because sometimes people also, uh, distinguish between pseudoscience and anti-science. I mean, what, how do you approach that problem?

40:17 Jacob Stegenga: OK, good. Um, THIS is a great question, and it's a very rich question. So, um, The demarcation problem has been a canonical problem, a canonical topic for philosophers of science. So, you know, introductions to philosophy of science courses in universities always will teach the demarcation problem. Uh, IT was a very important question for philosophers of science in the early decades of the 20th century through the mid-decades and later, um. So we always teach it to students. There are a few kind of big heroes in that topic. Uh, Karl Popper is one of the common names that comes up. Popper, um, in a sense, put the demarcation problem on the map, so to speak, and he had his own view, a theory of demarcation that's sometimes called falsificationism. His view is that good scientific work should be falsifiable, that is, When a scientist makes a claim about the world, that claim, there's got to be a possible way in which we can discover that that claim is false, um. And It does look like that is a desirable property of theories. Now what happened after Popper is that Philosophers made it their business of criticizing Popper's view, and it was fashionable for, for many decades to point out problems with Popper's view. It's interesting because Popper's view, this falsificationism view, um, got a lot of traction with scientists. So many scientists think that Popper was right, uh, falsificationism is right. You'll hear scient in your interviews with scientists, you probably get this all the time.

42:07 Ricardo Lopes: No, no, I mean that's, that's totally true. I mean, I, I many times I hear scientists or even science communicators just think is is just assuming that false. Falsificationism is true and is the correct criterion and even the only criterion to have in science because they're not even aware of the discussions among philosophers of science.

42:32 Jacob Stegenga: So yeah, yeah, that's right. And Popper, I mean, Popper's view was appealing. Um, THERE are, there are good, good things about it. You know, to give one kind of, uh, just to give the viewers of your show a little bit of like sense of, of how this debate has gone. Um You know, one of Popper's concerns was that when people don't try to falsify their work, when they go around trying to just like find evidence that supports their views rather than find evidence that falsifies their views. We can be led to a kind of confirmation bias. We can be cherry picking evidence that just supports our views, sort of ignore evidence that doesn't support our views. That looks really unscientific, right? So the, the, the, the opposite behavior looks sort of rigorous, right? So rather than try to confirm our views, we should be trying to disconfirm our views, um, and then, you know, the, the theories that survive that rigorous attempt, um, are the ones that we should at least, Entertained for a while for a while longer until we in fact falsify them. That was, that was what was appealing one of the things that was appealing to to Popper's view now. There are some in the history of science, there are some very successful scientists who didn't behave that way. So for example, one of my heroes is Marie Curie, and you know, in her diary, she speaks about, um, she had an idea. And she was so excited about the idea that she wanted to work extremely hard in her laboratory to confirm her idea, and that motivated her to really just, you know, put in many hours in the lab to devise really careful instrumentation to generate a lot of evidence that then confirmed her view. And so it's, you know, she wasn't behaving in a in a pauperian way, but she discovered really important facts about radiation and she won the Nobel Prize for this. So there are these like compelling historical cases that look like well they weren't, they weren't behaving as as Popper said they should. There's also sort of changes in. The kinds of things that we that broadly construed. We'd have to worry about when we're when we were worried about the demarcation problem. So I, I, I find the following kind of framing of the demarcation problem quite, quite, um, interesting. When Popper was writing. He was, he was formulating his demarcation problem, his his demarcation question and his solution in the 1920s and 1930s, and his big targets were, for example, Marxism. So he, he noted that

45:10 Ricardo Lopes: Marxism and I think that he also targeted psychoanalysis, Freudian psychoanalysis, I

45:16 Jacob Stegenga: think exactly, yeah, so he targeted Freudian psychoanalysis. Freudian psychoanalysis at the time was very um. Important, you know, many people were like, like viewing themselves as Freudians and seeing Freudian therapists, but of course Marxism was much more important, right? Marx Marxism was like this really important political theory that was clearly revolutionizing major parts of the world, um, and Popper was deeply worried about that. Popper was worried about dogmatism, about political dogmatism. He noted that Marx claimed that Marx's view was scientific and Marx, you know, generated like offered some putative evidence for the scientific status of his view, and Popper took his demarcation problem and argued that no, according to, he took his demarcation solution, namely falsificationism, and argued no, uh, Marxism, Marxist theory of history and politics is not scientific. So there it was, it's interesting because what was Popper responding to, what was, what was really motivating him, a worry about political dogmatism. And I don't know. I mean, at the time there was some sloppy science. There was, you know, phrenology, so on, but there was also really amazing science too, like the particle physics of the early 20th century or a lot of medicine of the early 20th century, biology of the early 20th century, chemistry of the late 19th century, geology of the 19th century. These were just like massive breakthroughs in all these major areas of science that were really, really impressive. Darwinian evolutionary theory of, you know, mid-nineteenth century. Popper was impressed by a lot of his work. Popper was impressed by Einstein, and he wasn't, you know, he wasn't using his principle of demarcation to do this like fine-grained assessment of particular areas of genuine scientific work like this bit of chemistry over here is good science, this bit of chemistry over here isn't. That's not what he was doing. He was saying Marx's theory of politics and history is pseudo-scientific, uh, Einsteinian relativity theory is scientific, right? So it's much more coarse-grained and targeted against what he viewed as dogmatism. Today. Of course we live with dogmatism, right, so dogmatism is still a major threat to us, but but something that has changed in science too though, right? Science itself has become. Um, THIS massive thing, uh, highly varied in its qualities, um, it, it's, you know, various forms of like social psychology and medicine is facing these replication crises. There are all these incentive structures in science that kind of corrupt science in various ways, and so. What we need now is not. This coarse-grained demarcation problem that puts like, you know, Marxism over here and physics over here, but rather we need an approach to demarcation which is a bit more fine-grained and sensitive to like particular differences within sciences. And that was what motivated my approach to demarcation in this book. Mhm.

48:35 Ricardo Lopes: Uh, BUT I, but I mean nowadays, and I know that there are different perspectives among, uh, or across, uh, uh, philosophers of science, but nowadays, uh, are there, uh, I mean, falsificationism specifically, is it still taken very seriously by Philosophers of science, or do they, I mean, at least have some caveats to it, or do they think that other, um, other theories are better suited to address the demarcation problem? I mean, how, what is the current state of affairs when it comes to it?

49:18 Jacob Stegenga: Yeah, you know, it's funny because, um, after the decades of criticizing Popper, a lot of philosophers of science basically just gave up on the demarcation problem, and I, I think that that's a real, that's, that's giving up a lot because in my view, the demarcation problem is one of the big problems for philosophers of science, right, especially when we live in society like today, like we need to be able to say something about the various qualities of scientific work. Um,

49:41 Ricardo Lopes: I mean, I mean, not least because nowadays even. People use a lot the term pseudoscience to refer to, for example, certain, um, medical or pseudo medical practices and things like that. So I mean, I guess that it is important to have a, uh, I, I mean, I, I guess not necessarily a universally accepted definition, but at least a definition of what pseudoscience is.

50:12 Jacob Stegenga: Yeah, that's exactly right, yeah. So, so, OK, so in short, Popper fell out of favor and Uh, sometimes some people will write things like, well, there, you know, he had a lot going for his view, but, um, so there's something right about his view, but that that's the sort of, you know, discourse. Um, IN some sense, I'm trying to resuscitate Popper. Um, WHAT I, what I, what I argue is that, OK, first, it's, it's worth noting just what was wrong with Popper's view. So one is that it was, Based on a single dimension, right, so we want to judge all of science and then we take this one property of scientific theories. Asking a single question, which is binary in nature, is this theory falsifiable or not, right? And also it's factive, it's about in a sense it's about the theory, right, rather than about the scientific work that promotes the theory or supports the theory. So it's sort of outcome oriented in that sense. So it's outcome oriented, it's a binary evaluation based on a single property. So if we get rid of those features of our approach to demarcation, we can keep falsifiability as an important feature for science. So it's what is one good making feature of scientific work, falsifiability, but there are others. So we can take a kind of multidimensional approach to scientific judgment. The other properties can be gradable properties, right? So for example, like how much evidence do you have? If you did a randomized control trial, do you have 1000 subjects or 100 subjects? So right, so, so scientific evaluation can be gradable based on multiple properties. Those properties themselves can be gradable, like how many subjects you have in a trial, um. And it can be process oriented rather than product oriented. So, uh, it can be about the scientific work, the experimental work that goes into offering some degree of justification for scientific claims. So that's my approach to demarcation, basically, it's to say, um, rather than a single property, rely on multiple properties rather than a binary evaluation, use a gradable evaluation, then, um, asking is this bit of work pseudo-scientific or not. Is a bit of a misnomer, um, because what what we're doing is really just asking like, is, does the work satisfy various justificatory principles and practices, one of which may be falsifiability, but there may be others, right, um, and if it scores low on our evaluation, then we could use the term pseudo-scientific or or we could just say it's bad science or whatever, um. Yeah, so that, that's my approach to, to the, the famous demarcation problem.

52:50 Ricardo Lopes: No, yeah, uh, I, I was just, um, it, it was coming to mind, um, a, a discussion, uh, it was not a discussion, it was more of a, a Q&A, I, if I remember correctly, that, uh, Richard Dawkins and Neil deGrasse Tyson participated in, uh, The last decade, I, I, I'm, I can't remember the year exactly, but at a certain point, someone in the audience was asking Richard Dawkins why, uh, science is true or why we should trust science, and he said, oh, because it works. And I mean, uh, yeah, that, that's fine, but that's an inductionist claim. I mean, it's because, because it works, that it's true. I mean, but, but, uh, I guess that scientists at least should be a little bit more aware of the philosophy of science or the philosophical foundations of what they're doing because I don't think that's just saying, saying, oh, science is true because it works. I mean because planes fly or something like that. I mean I don't think that's making justice to even their own work because it's much more complicated than that.

54:06 Jacob Stegenga: That's exactly right. Yeah, so I mean, one of the sections of that chapter on the demarcation question in my book gets exactly at this question about why should we trust science, um, and, Yeah, I, I totally agree with you that because it works is a bad answer. I think also the question itself is, is in a sense. Um, MISFRAMED because, so the question is, why should we trust science and. I, you know, going back to something we said earlier, science is a broad church, right? There's many different, like scientific disciplines, and within a scientific discipline, there's better experimental work, worse experiment experimental work, and so on. Science that it's cutting edge is like making these bold predictions, established science is sort of safe. Um, SO I don't think that we should trust science in that broad sense, right? It's not like there's this one thing, science, that we should either trust or not trust. Rather, I think we should take this finer grained evaluative attitude. Um, AND so then the question is like, what are the conditions under which some particular bit of scientific work is trustworthy, and what are the conditions under which it's not, right? So should we trust science? Well, I mean, I trust the results of particle physics from 1920. I think there's overwhelming reasons to think that a large majority of what has been passed down to us from physics at that time is true. It's, it's and it's common knowledge. These are timeless truths, or like, do I trust that the DNA is a double helix? Of course, right? Or like measurements of the charge of the electron. Have been stable at like 20 decimal places of a coolom for 100 years, right, so that's something that's and with you know measured in a bunch of different ways by a bunch of different teams decade after decade after decade, the sophistication of the equipment has gotten more and more and more. So anyways. There are some aspects of science that we should clearly trust, right? But you know, I wrote a book called Medical nihilism, and a big part of that book was like we shouldn't trust, um, the results of recent pharmaceutical science, at least in some areas of medicine like psychiatry, right? So or like a lot of social psychology of the last generation has been facing this replication crisis. So if some social, social psychologist goes on a TED Talk and says, I just discovered why, you know, blah blah blah, should we trust it? Well, you know. Probably not, not at least not right away, right?

56:30 Ricardo Lopes: Yeah, you probably shouldn't trust the research done on power poses and stuff like that,

56:38 Jacob Stegenga: yeah.

56:39 Ricardo Lopes: Yeah, but, but I mean, when we're talking about whether people should trust science or not, I, I mean, I, I, I guess that what I want to ask you is, what about laypeople, because I, I mean, when it comes to, for example, you and me, I mean, uh, you specifically as a philosopher of science, you've put thousands and thousands and thousands of hours probably into. Studying these, uh, topics and, uh, me personally, I mean, I've also done thousands of hours of podcasts and thousands of hours of reading different kinds of materials and there are certain specific topics where I can properly, I think, evaluate, uh, the evidence by myself after learning all of what I've learned from the conversation. With other people and reading all their work and so on, but what when it comes to lay people, because I mean we have to be fair here and the fair thing to say I guess is that the vast, vast, vast majority of people don't have the time or resources to do the same thing as we do. So I mean, to what extent. Should they trust the science or is it that they shouldn't trust the science, but they should deal with science in some other way that doesn't involve trust necessarily trusting it?

58:07 Jacob Stegenga: OK, yeah, it is a hard question when we speak about what should lay attitude towards science be and. I mean, one hope is that, you know, science education and also philosophy of science helps people. Um, THINK in a more careful way about when to trust science, like the conditions under which we should trust science, and so ideally, um, Everyone would be studying a bit of philosophy of science. Ideally, everyone would go buy my book and that, that would, that would, you know, give people a slight more sophisticated, um, uh, set of tools to think about what are the conditions under which they should trust science. What are the conditions under which policymakers. SHOULD formulate broad social policy on the basis of scientific testimony, you know, we all, the last time that you and I spoke was at the height of COVID lockdowns. Um, I was locked down in eastern Ukraine actually. I, I remember our video, um. And that lockdown was a result of policymakers formulating policy on the basis of scientific assertion, on the basis of scientific testimony. Now, with the benefit of hindsight, we have learned that a lot of the policies that were formulated at, at the time were probably misguided and the scientific testimony was, you know, often overexaggerated, um, like predictions about the harms of COVID, the modeling work, um, so, so asking this question. What are the conditions under which we should trust science is really, really important for policymakers and for the lay public, right, the lay public, Your average person is routinely faced with questions like, should you take this pill or not, um. And you know, should you get vaccinated?

59:58 Ricardo Lopes: Yeah, I guess that some of it also has to do with how science is communicated, particularly in more mainstream non-scientific media or media that is not specialized in science communication, because I mean, it's very common for you if you go to the comments section of a particular news piece. I, I mean people. Complain and with good reason that oh so yesterday they were saying that you should drink coffee. Today they're saying you shouldn't drink coffee. I mean, and it's with good reason because they get really conflicting information on on just mainstream media.

1:00:39 Jacob Stegenga: Exactly, yeah, so I've I've got like a great example of that that I'm going to come to you in just a minute, but let me firstly kind of get to your question, which is How should your your average non-expert um figure out when to trust science or not? So you've got the coffee example, like one day that they say it's safe, the other day they say it's harmful for our health, right? Um, SO one of my colleagues, Peter Vickers, I, I, I engaged with his work in one of the last chapters of the book, he argues that there's a few very basic. Signs that a non-expert can point to. One is consensus. So if there's broad consensus in a scientific discipline, that's pretty good evidence that the claim about which there's evidence is a claim that we can trust. So that's one kind of bit of a sort of sign. Another sign that Vickers points to is the extent to which there's broad diversity in that consensus. So if the consensus arises because like this small community of like-minded people just agrees about the claim, then that's not much of a sign that the claim is true or should be trusted, right, because that community could be essentially just involved in some kind of fancy form of groupthink, right? But rather if the consensus arises, so, so that's, that's Vickers that's two simple signs consensus. And The diversity of the community. Now in my book I fill, fill that thought out a bit more. So, um, I argue that, you know, if those social features of scientific communities are all working well, like the reasons for the claims are being publicly articulated, the putative reasons are being criticized by other members of the community, the reasons are being improved upon, the methods are getting better, the evidence is getting better. This iterates over time and the claim withstands all these tests over time. After a certain point, we really ought to start just trusting the claim in question. Um, SO there's a kind of withstand the test of time via this. Critical social approach um to, so another famous philosopher here who argues something like this is Helen Longino, she has a kind of social theory about scientific objectivity. I draw on this theory a lot in my book. So basically like you've got this social structure involved in this giving and taking of reasons, public criticism of reasons, improving upon reasons over time. If that's all in place. And some claim Gets passed down to us after decades of this, we should trust that claim. So all the best examples in science are like this, like the, the core of the atom is this dense nucleus, right? We've known that for 100 years, uh, the charge of the electrons, the sun is at the center of the solar system, dinosaurs existed, right? So all of those claims are claims that we should trust because they have those features. Now what about, um, for example, and this is, we might get to this example later, but A claim by an epidemiologist um in say March of 2020 that the COVID-19 pandemic is going to cause such and such a number of deaths, um. By June, unless we lock down, there's a claim that, you know, it's based on rapid work that hasn't gone through that public scrutiny, it hasn't gone through years and years of iteration of public scrutiny of reasons, um, oftentimes that work wasn't even peer reviewed or published, um, so it's a different kind of scientific claim, right? Um, SHOULD we trust it? Ah. Maybe, but it just doesn't look anything like the claim that say the structure of DNA is a double helix. Um, ANOTHER thing that you, you pointed to in, in your, in your last comment was about, um, The importance of Articulating norms for appropriate scientific communication when scientists are in this public facing mode, say, writing an editorial for The New York Times was a great example of, of, of this that I draw on in the book. Um, THE example is basically a breakdown of, of norms for appropriate communication in my view. So, you know, I argued that earlier we were saying that I, I argue that there's two good making features of scientific communication, the communication be justified and it be informative. Now, there was this huge experiment done during COVID. It's called the Bangladesh mask Study. So this was done in 2021. If you know, if we kind of jog our memory, we'll, we'll remember that everybody wanted to know, do masks work, right, do masks slow the spread of COVID. This is a huge debate. Some evidence suggested that that masks do help, some suggested that they don't. Experts weren't agreeing with each other. Um, SOME experts, experts were publishing meta-analyses saying no masks are basically useless, but then those experts got criticized by other experts. There's a lot of confusion. So this big study was done, the Bangladesh mask Study. 600 villages in Bangladesh, some were randomly allocated to a mask intervention. They got free masks, training on how to, how to wear the masks, and some had a control intervention. 350,000 people in these villages were in the experiment. It's a huge, huge experiment. No. Um, They finished the experiment, they got their data, they analyzed it. They published an article in the journal Science, the world's most important journal, and at the same time, they published an editorial in The New York Times. The headline of the editorial was, we did the research, masks work. That was the headline. OK. Now that's unambiguous, right? That's, here are these experts. We just published this article in Science. We did the research masks work, so it's totally unambiguous. That's super clear, right? But, but here, here's the, here's the catch. When they published the science article. They didn't actually include the number of cases of COVID in the mask villages and the non-mask villages. What they reported was the relative risk reduction, which I've argued in technical work in, in my, in my past. This measure, the relative risk reduction is extremely misleading. Um, SOME statisticians asked the scientists who did the study for the raw data for like the number of subjects that had COVID in the mask villages and the number of subjects that got COVID in the non-mask villages. So they shared the data, and it turned out that um the subjects in the, in the mask villages had something like, 1,085 cases of COVID. I, I, I think I've got that number wrong just by like a few, but it's that it's right around there. 1,085, um, cases of COVID. And in the non-mask villages there were 20 more cases. And this experiment lasted 6 months, so it's 6 months of masking. So you take 350,000 people, half of them get masks. You will force them to wear masks for 6 months. And then you get a reduction in Of number of cases of COVID by 20 out of 350,000 people, right? When you, when this is measured with what's called the absolute risk reduction, which is a more appropriate way to, to measure an intervention like this. It's basically like 0.0006% reduction in COVID. Yeah, yeah,

1:07:54 Ricardo Lopes: it's, it's, I mean, statistically insignificant, right? It's,

1:07:58 Jacob Stegenga: it's, it actually was statistically significant. Um, IT was a completely tiny signal. It was statistically significant because the numbers were so huge, but it was just a totally tiny. It was clinically insignificant, right? It was practically socially insignificant. But the scientists wrote that editorial in in the New York Times. We did the research, masks work, right? So. Using my principles, they just totally violated this principle of of justification, right, they made a claim that was just unjustified according to the evidence that they themselves had, right, um, yeah.

1:08:32 Ricardo Lopes: Uh, BUT I, I mean, since we're already talking about the COVID-19 pandemic, I, I wanted to ask you, and I, uh, and I mean, I, it's, I think it's very important here when posing this question to distinguish between Um, moral justifications and epistemic justifications, but, uh, uh, I mean, even from a moral, uh, I mean, I don't know if you want to include here the moral justifications or not. If you want, please, please tell us so, but do you think that because of the public health threat that COVID-19 posed, that kind of, uh, quote unquote past science was justified back then?

1:09:15 Jacob Stegenga: Yeah, OK, good. Yeah, thanks, thanks for this question. So, you're appealing to this term fast science, which, um, which, you know, I coined a few years ago. I first coined this in a blog post when. I was, you know, reflecting on the scientific work that was going on in real time as we were living through the COVID pandemic, so by fat science I meant. You know, loosely, the rapid epidemiological work um that um was giving us predictions about what was going to happen um to healthcare systems, to the, you know, number of deaths under different policy scenarios. Um, SO this was the modeling work that was being done by really prominent groups like for example, the, the Imperial College London Modeling Group, um, and, So that started in a blog post, then I wrote an article and I also included a chapter in my book on this notion, fast science. Now. The question is Is that fast science justified and As you rightly note, there's a kind of epistemic angle and a moral angle to it, to that, to that question. So Right, so what I do is I give a slightly more careful characterization first of what fast science is. So fast science is policy facing science. Which violates some principles of justification, um, so whatever standard principle, like, for example, if you like popper's falsifiability principle, fast science would violate that because it's quickly responding to a threat, um, so it's policy facing and it's, um, in response to a threat, and the, the threat has got to be, um. Like a it's gotta be imminent, so, uh, it's gotta be, there's gotta be a, the threat's gotta be threatening us. Very soon, and the threat's got to be of a very large magnitude in those scenarios when we have an imminent large threat. Then It does make sense to cut some corners to respond to that threat. So it might make sense to, for example, do some scientific work to figure out the nature of the threat and responses to the threat, but maybe don't publish it in a peer reviewed journal because peer review can take a long time, and maybe the peer reviewers, you know, reject the article, so you've got to submit it to another journal, right? And so that would look sort of absurd if we're truly facing an imminent threat. Um, SO, so I think that in, in general, the answer to your question is yes, fast science can be justified, um, justified on these kind of practical, social, moral grounds. So even if it, even if it's the case that we're violating some epistemic properties of science, like we're violating some justificatory practices of science, that can be OK if we're responding to, you know, if we're dealing with facing this imminent threat of great magnitude. So in general, I argue that yes, fast science can be OK, but not anything goes, right? So, so just because we've, we've said fast science can be appropriate in the context of a threat. Um, AND what fast science involves involves cutting corners. It doesn't mean that we can cut all the corners, right? Um, SO it doesn't mean that it's like we can just start consulting crystal balls, right, um, or, you know, fortune tellers, um, yeah, so that's what I argue in the chapter of this chapter of the book is that. Any science that's done, which is properly fast science responding to a threat has still got to satisfy various principles of constraint, um, it's got to kind of have the same look and feel of routine science, um, even if it's done quickly and even if some corners are cut.

1:13:05 Ricardo Lopes: Oh, OK, but just to clarify this point, because since our interview is mostly on, uh, from the point of view of epistemology, is, is it justified, it's justified morally, but do you think it's also justified epistemically or not?

1:13:22 Jacob Stegenga: OK, right, yeah, no, this is a good question. So I mean we could, yeah, so certainly fast science can be justified on moral grounds, um, so yeah, we've got to respond to a threat. Um, AND so we're going to say not submit this paper to peer review, or maybe we're doing an experiment. Uh, SAY we're testing, uh, a new vaccine, the safety of a new vaccine, and, you know, this happened, right? So we're testing the safety of COVID vaccines, but as we know, the harms of some vaccines can appear. Um, YEARS later, right, it doesn't necessarily going to appear. That's true for any medical intervention, right? Um, SO any medical intervention has a risk of harm that appears, um, years later, but we didn't have years to wait during the COVID pandemic, so, um, we needed vaccines quickly, and so we ran experimental trials of these vaccines, testing the benefits and harms of, of these vaccines. We did them quickly, so we had short-term follow-up and then we approved the widespread use of these vaccines. Based on the short term experiment, was that justified? Yeah, I think probably yes. um, I think it saved a lot of lives, um, and so, um, yeah, I don't want to say probably yes, it's definitely justified, um, so it's justified on practical grounds, um, does, but of course, there's still a lot that we can learn about the vaccines and so we can keep studying them in the longer term. And so we, our methods can get better and better and better, we can get more and more justified claims about the benefits and harms of the vaccines over time. Um, YEAH.

1:15:02 Ricardo Lopes: Mhm. OK, so I have, uh, 3 or 4 more topics I would like to touch on before we wrap up the interview. Um, I, I, this is one that I've already alluded to earlier, but do you think it's possible to have value-free science? And I, and I mean, I, I, I, I want to be careful here what I mean, what I mean by value-free. IS not because nowadays people discuss a lot. Oh, there are, I mean, individual people have certain political biases, certain moral biases. Some people are liberal, some people are conservative, and so on and so forth, and that can have an impact in the way they Um, I, I, I mean they design their studies in the way they arrive at their conclusions in their studies and so on, but that, that's, that's not necessarily what I'm talking about. What I, what I'm asking you is whether science as an institution can be value-free. I mean, in terms of uh in constitutive terms, whether it's possible for it to be value-free or not.

1:16:13 Jacob Stegenga: Yeah, yeah, good, yeah. OK, so I, um. I'm going to answer the question. I'll, I'll just add to your, um, the way you've framed the question a little bit. So, so in general, the question is, can science be value free, um, and there's this very interesting debate among philosophers of science that are debating that question. What's not being debated are questions like, Um, is the scientific research agenda value free? Of course it's not, right? So we decide to study this topic rather than that topic, and that's a value laden decision. So scientists, you can get research funding to cure cancer or. Build more explosive bombs or whatever,

1:16:52 Ricardo Lopes: yeah, yeah, as we said earlier, they're all, there are all sorts of political and economic motivations that go into that, so

1:16:59 Jacob Stegenga: exactly, so that's one way in which values obviously influence science, or another way is like ethical constraints on what scientists can actually do, right? So there's like certain kinds of experiments we just can't do to people or to animals or to the environment or whatever. So those are valulaating constraints on science, that's obvious, or like certain technologies that we develop on the basis of science, um. So those are also value laden, right? We decide to develop this technology, we decide to constrain this technology. Those are value laden influences on the application of scientific results, but none of that's controversial in any kind of deep, interesting philosophical sense. The really deep, interesting question is, Should those kinds of values like social, ethical, political values influence scientific reasoning itself, right? So scientific reasoning itself, the kind of old school view about scientific reasoning itself, it's pure from value influence, it's just, it's value free, it's objective in this sense, right? It doesn't depend on whether or not I like this political candidate or I, I like chocolate ice cream, you like vanilla ice cream, no, it, it's just, it's about the objective facts, right?

1:18:01 Ricardo Lopes: So that's Yeah, yeah, let, let me just say that that was exactly what I was trying to ask, but you put it in better or more rigorous terms, so yeah,

1:18:11 Jacob Stegenga: that's what I'm paid to do. Yeah, um, but no, yeah, yeah, your question is, is exactly on point. So, um, Somehow in the last generation, among many of my colleagues in philosophy of science, they have come to reject what's called the value-free ideal. So the value free ideal is that old school traditional view that says science is objective, it's scientific reasoning is objective, scientific reasoning is free from value influence, that's the value-free ideal, and it's become almost status quo in philosophy of science to reject the value-free ideal and. I think that that is all wrong. So with a, with a very close friend of mine, Tro Mennon, we have been publishing a series of articles that um try to resuscitate a particular version of the value free ideal. We articulate a new version of the value free ideal that we think are immune to the existing criticisms, so we're sort of saving the value free ideal. Some of that work with Troun then fed into. Uh, ONE of the central chapters of this book. So in the, in the book, in one chapter, I'm saying, Science at its best, scientific reasoning at its best. Can be value free And I think that many of the examples of common knowledge that we we've been speaking about today. Illustrate this perfectly. So like there's no value influence in the claim that The structure of DNA is a double helix. Um, THERE'S no value influencing the claim that You know, the Earth is at the center of the solar, I'm sorry, the sun is at the center of the solar system, um, so, you know, there's a like established contributions to common knowledge. They're value free. The reasoning that goes into them is value free. Um, SO yeah, Tyro and I have been working for years. Um, ACTUALLY we started writing these articles also during lockdown. We would just zoom for, for hours in the way that you and I are zooming now and come up with, come up with ideas and ways to try to salvage the value-free ideal, and now we've got a series of 44 articles and, and, and this chapter in the book in which we're trying to say science can be value free.

1:20:26 Ricardo Lopes: OK, so, uh, I, I also wanted to ask you about scientific progress. I mean, uh, and of course here we have to get into one of the most prominent and famous approaches to scientific progress that come, that came from Thomas Kuhn, but, uh, I, I mean, What, I, I, I guess I have two different kinds of questions here. So, or, or three even. What, so first of all, what is scientific progress and how do you think we should approach it?

1:20:58 Jacob Stegenga: OK, yeah, yeah, so you mentioned Kon and uh you, you're noting that scientific progress is, yeah, a core topic for philosophy of science. Um, WE do want to know like what is scientific progress. Like science appears to be progressing somehow, right? It's getting better and better at doing something. Um, WHAT is it getting better and better at? What actually, how should we understand scientific progress? Um, MOST accounts of scientific progress are factive, to go back to that term we used earlier, right? They're based on. Uh, BASICALLY a view that science makes progress when it accumulates more truths, more knowledge, more facts about the world. Um, SOMETIMES some people have an understanding-based view of scientific progress. They say science makes progress when it, uh, when our scientific understanding improves, but by understanding they typically have a factive notion of understanding. So most accounts of scientific progress are factive, um. And One possible definition of scientific progress could be based on my account of common knowledge. So if I say that the constitutive end of science is common knowledge, then one could just build in an account of progress that says, well, we get scientific progress when we get an accumulation of common knowledge. Um, THAT would also be a factive account, that would be a factive approach to, to, to scientific progress. Um, I. I'm not deeply opposed to an account like that, I guess I think my, I might be OK with an account like that. What I try to do in the book is argue for an account of scientific progress that is not product oriented, but rather is process oriented, which is focused on justification. Rather than The accumulation of truths or knowledge, common knowledge or whatever. So what I, what I argue is that. Justification is like epistemically accessible to us in real time, so we can judge and scientists can know if they're making progress in real time because they can judge the extent to which some some scientific work is truly justificatory or not, um, but they can't in real time know if their claims are true because again it takes. Usually truth is ascertained in science retrospectively, right? Like there's got to be this decades of iteration, public criticism of reasons and so on. So it can take years or decades, like, you know, Watson and Crick first published their paper in Nature articulating the structure of DNA that was in 1953, but it was only like 10 years later that they won the Nobel Prize. You know, there's examples like this, it takes time for scientific communities to really establish that they've got common knowledge, um, but we want to be able to judge progress in real time. So like I think about, for example, a mountaineer who's climbing a mountain. That mountaineer. Wants to know if they're. On the right path if they're, if they're, you know, approaching the summit, they want to know that their effort's not wasted, you know, um, and so, Being able to determine that there. Um, ON their way up the mountain rather than getting lost is really important for them, and I think the same is true for science. So I think that scientists should be able to judge progress in real time and a justification centered account of progress is accessible in real time. Um, SO that's the, that's the kind of argument that I give in that chapter.

1:24:24 Ricardo Lopes: And what do you make of, uh, I mean, I'm just asking you about him because of course he's very prominent when it comes to this question specifically, but what do you make of Thomas Kuhn's suggestion that, uh, I mean, basically science goes through revolutions and paradigms change and all of that. So I mean, what do you make of his suggestions?

1:24:50 Jacob Stegenga: I'm really glad you're asking this question because um In addition to Popper, Kon has perhaps had. You know, the most influence or, or a large amount of influence on both scientific thinking about science and about public perceptions of science, public views about how, so, right, there's this revolution-based view that's, um, thanks to Thomas Kuhn, so the idea is like science can, can work within a paradigm for a while, decades or centuries, and then. There will be all these anomalies that crop up after like the equipments get better, more experiments are done, more evidence is gathered. There's this accumulation of novelties that the existing paradigm can't accommodate, and then that will just lead to this kind of breakdown, this revolution, scientists like completely rethink um foundations of their domain from bottom up. They articulate new theories, that's, and then that generates a revolution. The old theories are chucked out, so there's lots of examples of this like the rejection of Newtonian physics, um, um, by, you know, Einsteinian physics, for example, there's lots of examples like this. That was Kuhn's view about science, um. I suppose so. I basically think that he exaggerated. I think he, he, he was exaggerating about the frequency of revolutions, about how widespread revolutions are, and I also think that, um, For certain domains of science, for certain kinds of claims, they're revolution free basically because, because key theoretical claims in some domains are common knowledge, they're contributions to timeless truths. So like there will be, there will never be a revolution, a scientific revolution about the structure of DNA, for example, not the macrostructure of DNA. Like the macrostructure of DNA is a double helix and that's a timeless truth. It's never going to undergo a revolution in this. So, you know, Koon's focus was on like. Abstract physical theories, um. You know, the switch from Newtonian physics to special relativity, for example, it could be that for physics at its cutting edge. These are, this is a domain, either physics about the tiny world, you know, the smallest components of of matter, or physics at the largest scale, like the structure of the universe on the far side of the universe. So it could be that we're like the structure of the universe a nanosecond after the Big Bang, right? So it could be that for certain kinds of questions at cutting edge physics, astrophysics and particle physics. That those claims at the cutting edge are just always going to be risky, epistemically risky in the sense that they're making bold claims about Like spatiotemporal features of the world that are really removed from us, so there's a kind of evidence theory disconnect, so to speak. Philosophers sometimes use the word underdetermination. The theories are massively underdetermined by the available evidence. So it could be that for those domains, Kuhn was right, that we'll go through revolutions. Um, THAT, that's, I'm, I'm willing to go with Kuhn on that. But for spatiotemporal scales that are much more accessible to us, even like, you know, the structure of the DNA or the structure of the atom. Uh, OR, you know, the existence of dinosaurs, um, middle sized, so to speak, like middle sized relative to the absolute smallest scales and biggest scales, um, for those kinds of domains, we can generate robust. Knowledge, common knowledge, our, our common knowledge about these domains are timeless truths. They're not going to go through revolutions. Um, SO I think Kohn was exaggerating about the how, um, widespread this notion, uh, how widespread this notion applies. Um, THIS is really worth saying, and you know, I, I lay all lay all this out in the book because we think about, you know, to, to kind of repeat. The two biggest influences on thinking about science at this meta level are Popper and Koon, and when we take the two together, we've been discussing their ideas quite a, quite a bit, when we take the two together. It's tempting to conclude that Science can never really attain truths that will really withstand the test of time in the long run. There'll always be revolutions, you know, Popper wanted to say that we don't even have like confirmed theories, right? We can only sort of tentatively entertain theories. So, as you, as you know, you know, Konn had this radical view that we're always going to chuck out theories, always go through revolution. So, That's the view that we might inherit if we take them too seriously, and I think that that view is wrong, um, and that view can generate this like deep distrust of science, right? Like why should we trust science if it's just a graveyard of dead theories, if it's just going to go through one revolution after another, right? Um, WHY would you even get on an airplane if, you know, our understanding of aerodynamics is just going to be completely overturned next, next decade or century or whatever, right? So, Science is not just a graveyard of dead theories. In fact, quite the opposite. Science at its best has these achievements, what I call common knowledge, or timeless truths that are always going to be with us. They're just permanent achievements. And so I really think that Kuhn, although Kuhn might have been right about something and Popper might have been right about something. Um, WHAT I'm trying to do in the book, in at least a chapter or two of the book, is to really push back against their exaggerations and offer a kind of a more realistic view about science, but a science that also like a view about science that acknowledges the achievements where they are.

1:30:51 Ricardo Lopes: Mhm. Yeah, and I mean, I don't know if you will agree with me on this point, but, and I'm not sure whether it's it's something that could influence directly or not, or if it's, or if it comes from other uh erroneous ideas that are very common about that people have about how science works or should work or is expected to work, but I, I mean, I think that even many scientists themselves, uh. HAVE this common idea that they look at science. I mean this happens. I see it happening more with older scientists or the ones who are even already retired, where, for example, they look back in evolution, I mean, I'm just going to give an example in evolutionary biology they look back and they say, oh, in the 1960s, 1970s there were all these great Development, sexual selection, kin selection, and all of that, and now it's, uh, I look at the present moment, the past few decades, and it's basically stagnated. But I mean, it's not really, because if you talk with people that are doing the work right now, you have more knowledge in terms of how, for example, speciation works and you have many great developments in terms of developmental biology. Developmental bias, for example, and how it might influence the evolution of species and you have things that some people like to associate, others don't don't really like to do it with the extended evolutionary synthesis. I mean work on niche construction and all of that kind of thing, but I mean, of course, those that work is not as sensational as all sexual. Selection, natural selection, kin selection, the, the theory of evolution itself. I mean, I guess, yeah, it's, it's not as exciting. I mean, but that's just to be expected because I mean, most science actually, to be perfectly honest, is kind of boring. I mean, it's interesting but boring at the same time. I mean it's, it's a bit weird, but, and, and also, I mean, I guess that. Some of those people, like I don't know, even going all the way back to Darwin, I mean they were in a sense in a privileged position because they were in a context where those major theories weren't developed yet and so they had the Opportunity to do so and I mean now people cannot go back in time and recreate the theory of evolution by natural selection. I mean, it's already there. It's not going to happen again. So I mean, but I guess that. Even some scientists themselves have these or get these erroneous ideas that oh if we don't have this sort of bombastic findings occurring all the time, that science is not progressing at all and I, I mean, I don't look at it that way at all. I mean, do you agree with that?

1:34:08 Jacob Stegenga: Yeah, yeah, definitely, yeah, yeah, yeah. I mean, of course, it's wonderful that, um, you know, we, we've got, you know, Darwin, Darwin, Darwinian evolutionary theory was this massively important thing, and I totally agree with you that um it's not going to be overturned. It's not like there's going to be a revolution about Darwinian evolutionary biology. Um, BUT there will be lots of more fine-grained things that we can, um, come to understand about, about evolution, um, and so, uh, I, yeah, taking a finer grained approach, that's part of the spirit of the book, right, is we should be taking whenever we're thinking about scientific evaluation, the goods of science, scientific demarcation, progress in science, our approach should be sort of fine grained, right? We should be like kind of zooming in a bit.

1:34:54 Ricardo Lopes: Mhm. Yeah, so, I mean, I have two more topics I would like to ask you about. So, uh, I, I think at least this one is important also to explore here. So in the book you also talk about, uh, the allocation of credit in science. So, I mean, uh, tell us what, uh, credit allocation is, uh, particularly in the case of science, and, uh, what do, uh, what do you think are the rules it should follow?

1:35:25 Jacob Stegenga: Yeah, good. OK, so, you know, credit, credit in science comes in many forms. Um, SO it can be, uh, you know, it can be a prize. These are famous examples of credit, credits like the, the Nobel Prize. There's a bunch of different kinds of prizes. It can come in more mundane forms like getting a good job, getting a promotion, getting tenure, uh, getting a job in a fancy university, um. It can get, it can come in sort of softer forms like um having lots of interested graduate students, right, uh, graduate students want to come work with you because you've done some interesting scientific work. um, SO yeah, credit comes in, in different kinds of forms. I mean, other, you know, other familiar examples are like. The naming of things, um, this was, uh, you know, a famous example of credit, right? So the naming of particles, the naming of, um, you know, various astronomical things like Halley's comet, right, um, so naming of entities, naming of discovered species, for example, that's a way of kind of honoring, um, scientists on conferring credit on them. So there's all sorts of ways in which credit is conferred in science. Credit comes in many different forms. Now, there's this very famous theory called the the priority rule. Um, THERE'S a sociologist of science, Merton. Who argued that the priority rules is the way that credit is allocated in science. The priority rule says winner takes all. The first person to discover something gets all the credit for for that discovery. And philosopher, philosophers of science have picked up on this idea and have run with it. They've said, yeah, Merton was right. The priority rule is how credit is allocated, and here are the various goods that come out of that priority rule. So it incentivizes the search for novelty. It motivates scientists to work really hard because, if it's only winner takes all, you've got to be the winner, right? So philosophers have adopted that as a premise. The priority rules in fact how the credit is allocated in science and then argue that this has normative implications like these good features about science that come out of the priority rule. I think that that's all wrong basically. So I spent a chapter arguing that this whole way of thinking about credit allocation in science is misguided. Um, SO what I argue that it's descriptively false, it's false that credit is allocated according to the priority rule, and I also argue that it's. That's, it's a good thing that credit is not allocated according to the priority rule because there are better ways to allocate credit um that have, that are more appropriate. Um, SO I give a descriptive, it's a descriptive thesis and a normative thesis in this chapter. Um, SO the descriptive thesis is I get, get it off the ground just by various appealing to various examples. Um, SO, so like one example that I like is the discovery of radiation. Um, SO I think there's a detailed historical example. It's really, really fun to read about this example. I give, I give it a, you know, maybe 2 or 3 pages in the book, but basically there was this, there's a scientist, Becare working in, in France. Who Sort of haphazardly discovered the phenomenon of radiation. So he was the first to actually generate. Existence, generate evidence for the existence of radiation. But he didn't do the hard work to understand radiation, to make sense of it, to generate like really careful evidence pertaining to radiation. Marie Curie did this. So Marie Curie Curie, um, working in with, with her husband, um, um, carefully. Generated really systematic evidence using novel experimental apparatus, uh, over, you know, months and years. And that was her PhD project. She ended up winning the Nobel Prize for this. Um, SO she was in a sense the second comer. Um, SHE wasn't the first comer, right? Becare was the first, but Marie Curie did all the hard work and um, she was rightly allocated credit for for all that hard work. So there's a case in which the second comer is given credit. And rightly so, right? So it's a descriptive thesis and a normative thesis. Like it's a good thing that we have, we gave credit to Marie Curie. Um, SO that's the, that's the kind of argument I give in that chapter. Um, ANOTHER sort of feature about the priority rule is that if you allocate credit according to priority rule, there's another feature. It's factive, right? So you're allocating credit according to the first person to discover a true claim, right, um. But actually What the, you know, what we're rewarding Marie Curie for was the quality of the work that she did. So it's again, it's process oriented rather than product oriented. So that's, that's another kind of slogan that I use. So that's another feature about the priority role. It's factive, but credit allocation is not and ought not be factive oriented. It should be process oriented. And then finally, If you give credit according to the priority rule, credit allocation, credit is like non-partable, so it's non-sharable in this sense. If it's winner takes all, then there's effectively only one winner. Now, of course, it's trivial that scientific groups, scientific teams can have more than one person on it, right? So you can share a credit in that sense, like when. The Nobel Prize was awarded for the discovery of the structure of DNA. Watson got it and Crick got it, right, and a third person too. So there's credit was shared in that sense, um. In the sense that there's multiple people on a team like generating evidence and theorizing about that evidence, but In fact, credit can be shared in a broader sense too, right? So it's not like we just forget completely that a second person was involved in, like in a completely separate independent team involved in generating evidence or theorizing about some novel claim. So credit is in fact shareable, and I give other examples of this um uh in, in the book. Um, SO yeah, the example I give, it's a really fun example, it's the discovery of nuclear nuclear fission. And this was like one of the most important discoveries of the 20th century, right? I mean, it led to the development of the atomic bomb, right? So it had this huge impact on, on our society and on World War II and so on. But when we ask like who discovered fission, it's almost a senseless question. Because, you know, there's one scientist, Fermi, who was developing some really strange evidence in his lab that he couldn't make sense of. He published a paper in which he thought that he was generating elements that were like heavier than uranium, which he wasn't. He won the Nobel Prize for that. So, you know, he did interesting work that like helped contribute to the discovery of fission, but he got like his theorizing about it was all wrong, um. Then there were these chemists and physicists who were trying to theoretically understand what Fermi had been generating in his lab. Um, Ida Nodak, uh, a character who appears in my book, um, was, um, such a person. She, she in fact got it right, so she was like basically the sort of progenitor of the, of the theory of fission. Um, THERE were other, others involved as well in generating more evidence for the existence of fission, improving upon Ferrami's methods. Um, WHO discovered fission? It's sort of a senseless question. There were these independent teams that were kind of, you know, this one was generating some evidence. This one was doing some theorizing. This one over here was trying to fuse the two. Credit is shareable, right, um, and it ought to be shareable, and it very often is, um, shared. Yeah, so that's, so I really in that chapter, I go after the priority rule, um, and I think that's, it's, it's important to do so. Again, it's about like celebrating the best of science, um, in appropriate ways, like, um, in ways that makes sense.

1:44:05 Ricardo Lopes: OK, so one last, uh, topic that I would like to explore. You've already, uh, talked about, or a little bit about timeless truths in science. You gave the examples of, for example, the structure of DNA, the structure of the atom. I mean, I guess we could also talk about, for example, the particle physics in general, the theory of evolution by natural selection, I mean, uh, things like that. But, but do you think that Uh, those examples like those are. The rule in science, or are they the exception? I mean, do you think that they are representative of most of the knowledge that is generated in science, or that perhaps most of the knowledge is always up for being I, I mean, even more than debated being replaced by better knowledge in the future. I mean, the, the, and um most scientific knowledge is not actually or does not correspond to timeless truths.

1:45:14 Jacob Stegenga: OK, yeah, yeah. So again, I might go back to this kind of distinction between science at its cutting edge and, you know, science that's gone through these years of iterations of articulated criticism and improvement upon the, you know, reasons offered for various claims. So I think probably science at its cutting edge, especially science again to go back to this idea of underdetermined theories, right? So science that's, you know, about the very smallest aspects of nature or the very biggest, most distant aspects of nature, both in space and time. Scientific claims in those domains are at the cutting edge, are extremely unlikely to be timeless truths. The vast majority of scientific claims in those domains are not timeless truths, um, but in more accessible spatiotemporal scales, like all the examples that we've been using, um, the structure of DNA, dinosaurs, things like that, um. Those are timeless truths, and, and sometimes these timeless truths are in fact really, really like about. ARCANE, sophisticated like aspects of nature. So like I use the example of the measurement of the charge of the electron, right? So imagine we can measure the charge of the electron to 20 decimal places of a coom. This is. Astonishing, right, and we've been able to do that for 100 years, right? So it's, it's a, it's, it's. So if we The charge of the electron has been steady. The estimates of the charge of the electron have been steady at 20 decimal places of a coolom for 100 years. So at 16 decimal places of a coolom, that's just definitely a timeless truth, right? That's absolutely. And so science is filled with examples like this. So if we, if we kind of step away from the radically underdetermined domains of science and step away from The cutting edge of science. The The claims that have withstood withstood the decades of iteration of articulated reasons and criticism for those reasons in this like social context. Many of those are timeless truths. Um, THOSE are the, those are the claims that often can get into textbooks, for example. Um, SO yeah, there, there are timeless truths, and this goes back to something we spoke earlier about. For some reason, colleagues in sociology of science and philosophy of science and history of science for many years have been really nervous about this idea of timeless truths. They've often denied that there can be timeless truths. Um, AND I think that we should get over that nervousness. Um, I think it's a kind of legacy. It's in part a legacy of those big guys, Popper and Coon, and we should get past that very misleading legacy for many of the reasons that we've been speaking about. And again, like, celebrate the achievements of science at its best while maintaining the view that we should also be cautious about science when it's not at its best, like. Like science during the COVID pandemic, for example, um, yeah, so this is the kind of like, yes, there are timeless truths in science, and then yes, there are big swaths of science that we should be very cautious about.

1:48:29 Ricardo Lopes: Mhm. OK, great. So the book is again Heart of Science, Philosophy of Scientific inquiry, and of course I'm leaving a link with the description of the interview. Uh, AND Doctor Steenge, would you like to, uh, tell people where they can find you and your work on the internet? I mean, I, I'm asking you because in our first interview, it was already 6 years ago, you have a, uh, an Another affiliation. So that give us, give us an updated version of where people can find you on the internet. Oh

1:49:02 Jacob Stegenga: yeah, I mean, like my, my main website is available um just by Googling my name and, um, a lot of my, most of my published articles are freely available on my website. So if viewers want to just look at those articles they can, they're just available on my website. The book is, um. You know, actually it's a very cheaply priced book for academic books. Um, IT'S, you know, many, many monographs in philosophy of science are $100 150 dollars. This is, I think, 3030 $30 US dollars or $25 US dollars, something like that. So that's available just on Amazon or the University of Chicago website. Um, YEAH, so that's, that's how they can find the book, yeah, and, um, about me, and so when we spoke last, I was at the University of Cambridge, and I've moved recently to Singapore, so I'm at the, as you mentioned in your introduction. Uh, Nanyang Technological University in Singapore. Yeah.

1:49:55 Ricardo Lopes: OK, great. So thank you so much for taking the time to come on the show again. I mean, it's, at least for me personally, this has been a very fascinating conversation.

1:50:05 Jacob Stegenga: Thanks, Ricardo, really fun talking with you. Thanks for having me on the show.

1:50:10 Ricardo Lopes: Hi guys, thank you for watching this interview until the end. If you liked it, please share it, leave a like and hit the subscription button. The show is brought to you by Enlights Learning and Development done differently. Check their website at lights.com and also please consider supporting the show on Patreon or PayPal. I would also like to give a huge thank you to my main patrons and PayPal supporters, Perergo Larsson, Jerry Mulleran, Frederick Sundo, Bernard Seaz Olaf, Alex, Adam Cassel, Matthew Whittingberrd, Arnaud Wolf, Tim Hollis, Eric Elena, John Connors, Philip Forrest Connolly. Then Dmitri Robert Windegerru Inai Zu Mark Nevs, Colin Holbrookfield, Governor, Michel Stormir, Samuel Andre, Francis Forti Agnun, Svergoo, and Hal Herzognon, Michel Jonathan Labrarith, John Yardston, and Samuel Cerri, Hines, Mark Smith, John Ware, Tom Hammel, Sardusran, David Sloan Wilson, Yasilla Dezara Romain Roach, Diego Londono Correa. Yannik Punteran Ruzmani, Charlotte Blis Nicole Barbaro, Adam Hunt, Pavlostazevski, Alec Baka Madison, Gary G. Alman, Semov, Zal Adrian Yei Poltontin, John Barboza, Julian Price, Edward Hall, Edin Bronner, Douglas Fry, Franco Bartolotti, Gabriel P Scortez or Suliliski, Scott Zachary Fish, Tim Duffyani Smith, and Wisman. Daniel Friedman, William Buckner, Paul Georg Jarno, Luke Lovai, Georgios Theophannus, Chris Williamson, Peter Wolozin, David Williams, Dio Costa, Anton Ericsson, Charles Murray, Alex Shaw, Marie Martinez, Coralli Chevalier, Bangalore atheists, Larry D. Lee Junior. Old Eringbon. Esterry, Michael Bailey, then Spurber, Robert Grassy, Zigoren, Jeff McMahon, Jake Zul, Barnabas Raddix, Mark Kempel, Thomas Dovner, Luke Neeson, Chris Story, Kimberly Johnson, Benjamin Galbert, Jessica Nowicki, Linda Brendan, Nicholas Carlson, Ismael Bensleyman. George Ekoriati, Valentine Steinmann, Per Crawley, Kate Van Goler, Alexander Obert, Liam Danaway, BR, Massoud Ali Mohammadi, Perpendicular, Jannes Hetner, Ursula Guinov, Gregory Hastings, David Pinsov, Sean Nelson, Mike Levin, and Jos Necht. A special thanks to my producers Iar Webb, Jim Frank Lucas Stinnik, Tom Vanneden, Bernardine Curtis Dixon, Benedict Mueller, Thomas Trumbull, Catherine and Patrick Tobin, John Carlo Montenegro, Al Nick Cortiz, and Nick Golden, and to my executive producers, Matthew Lavender, Sergio Quadrian, Bogdan Kanis, and Rosie. Thank you for all.