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Who Gets to do Science? Part 2 · Through the IDEA Lens

• speaker-0: Mm. speaker-1: For example, 50% of the discipline in in physics, 50% of ⁓ physicists being women, and the estimates are well over a hundred years. We're really not moving very fast. So we might actually, you know, I don't know, go to Mars before we have gender equity in physics. Who knows? speaker-0: Welcome to Through the Idea Lens, the podcast that brings science and inclusion into focus. Join hosts Dr. Mahadeye Sukai and Peter Field as they explore inclusion, accessibility, and belonging in STEM and speaker-2: Welcome back to the Through the Idealens podcast. Last time, Mahadeo and Shahini came to the conclusion that good science is inclusive science. Today they're going to explore this even further. Then Shahini is going to talk about the importance of curiosity. Let's listen in. So Shahi, where have you seen it practiced well and and how do you practice this in your own speaker-1: Well, I mean, there's not actually specific measures or metrics necessarily. It's ⁓ I mean sometimes yes you can certainly measure certain outcomes, but there's many it's it's a complex thing to try to assess whether inclusion is being done well in the context of science or not. So it's not so easy to identify it all the time. But I guess I can still try to give you some concrete examples which I think is what would be helpful. So in my own field, ⁓ physics and astronomy, for example. In astronomy, there's this really cool example of citizen science, which has been used quite successfully. And that actually, in a way, it's broadening, as Mahadeo, you were ⁓ referring to, what we talk about specifically or traditionally as the scientific method, where there's, you know, these trained expert scientists who do all of the science, right? They're sciencing. But actually the citizen science method is broadening even that definition of who does science and how is science done. You don't necessarily need expertise. So in astronomy there's this ⁓ really cool website called Galaxy Zoo, where anybody can go in and look at images of galaxies and try to classify them. And this approach, of course, brings in folks who perhaps would not have dreamed of doing actual science with any kind of rigorous approach or anything like this. But turns out This way of using everybody's individual lens to try to find patterns is incredibly powerful. And in fact, it has led to, you know, faster data analysis, new kinds of discoveries. So to me, that's a very exciting example of we can really rethink science itself and what we mean by inclusion by trying these different kinds of approaches. I mean, there's many other examples, certainly in healthcare, women's health. We know that there was a huge change and improvement in women's health once women doctors actually were involved. And you know, you saw many more, especially and of course, ⁓ you know, focused on not just health in general, but specifically related to gynecology, for example, and these fields that really there was a huge change. And you know, if you think about it, it's it's obviously common sense in that case, right? Where there's clearly a benefit in having women involved in women's health. But there's many, many such examples in throughout science in all the different ⁓ disciplines. Those are just a couple to throw out. I know Mahade already mentioned the Genome Project, but there's probably other examples too. Mahadeo? speaker-0: Well, I mean, so let's step away from human beings for a moment and talk about agriculture and talk about botany, right? And so the most heavily industrialized parts of the planet happen to be in the northern part of the northern hemisphere. So tropical plants in, for example, the rainforest in the Amazon and in the rainforest of of Southeast Asia, those are not things that have been as well studied just because we're not interested in them as much as we're interested in maize. Right? Corn. So what we study, where we put our emphasis, sometimes sort of comes with those human pieces of baggage that mean that this conversation around inclusive science isn't just limited to humans. It's about the choices that humans are making with respect to interacting with the natural world. I think that's something that's actually worth it to just sort of think through and think about. The notion of citizen science, by the way, I I will say and Shahina, I can't remember if if I mentioned this to you. Originally I was supposed to be an astronomer. So the thing was I I ran into a computer science prof in first year university who turned me off computer science, not because I was bad at it, but more because they couldn't teach. And then I I made it a choice to go away from the thing that I originally loved and go do something else that I loved and came to love just as much. And in fact, what I was actually trying to do is escape computer science. The problem is these days you can't escape computer science in a lot of spaces. So, you know, but when I started to get into The work around accessibility in science, technology, engineering, and math, which is now for me a 20 somewhat year journey. Nobody, institutionally or any funding organization, was remotely interested in the question. And it's been a 20-year citizen science journey of doing work around accessibility in STEM and turning it into what is now a professional discipline to the point where there are folks who will do. part of the their academic research is going to be accessibility in research or accessibility in STEM. And so it's now a legitimate space, not well funded, mind you, but it's still a legitimate space. When 20 years ago when I started it wasn't. There's power of citizen science in other spaces, not just astronomy. But we want to talk about other ways and other places where inclusive science can be highlighted. I mean, public health during COVID was a good example of this, right? Health disparities during COVID. Understanding health disparities, understanding social determinants of health, like recognizing that people are people and everybody's different. So understanding biological outcomes, social outcomes, health outcomes through that lens of people are people and everybody's different, there's one space there where it's become quite clear that paying attention to the diversity of the human experience is actually quite valuable, quite important. I think if we're looking for the other side of it, if we're looking for scientists who are building very good, very strong, you know, very diverse teams that are built on premises of inclusion, right? We're we're looking at people who actually do work within this space. We're looking at people who have thought about this, who've built up this fundamental premise that diversity and difference matter in human research. I I think about folks, a couple of friends of mine in the States who I'd love to have on the podcast one day, Bonnie Lynn Sweener from Johns Hopkins, John Ross Rooseau from New York University. Both of these individuals are are folks who are scientists with disabilities who are integrating the conversation of inclusion of persons with disabilities directly into the work that they do. You know, I've done that. I didn't really want to say that on my own podcast, but I've done that. And, you know, I'm not the only one because there are others who've done the same things. If we go looking for them, those examples exist. Maybe we don't call them out enough within the scientific space to to say this is a legitimate way of doing our business. And it works as well as, if not better than, the existing and sort of establishment ways of doing things. And so maybe that's something we need to do. speaker-2: So Shaheene, I think you do a fair bit of work around women in science. Can you talk about that for a minute? speaker-1: Yeah, so I think that in many areas of science, ⁓ if you look at the demographics, you're gonna find that women are pretty underrepresented. I mean, it's not the only underrepresented group, but given that women are not actually a minority demographic worldwide, we ⁓ have a real problem in many disciplines, including my own in physics and also computer science, many areas of engineering. We still see that the proportion, for example, in physics globally of women physicists is around twenty percent. And what's more worrying is that it's hovered at that level for as long as I've been in the field, honestly. So it's not that there's been much of an improvement. There's been a slow improvement, but folks have done estimates to try to guess when in the future we may actually get to, for example, fifty percent of the discipline w in in physics, fifty percent of ⁓ physicists being women and the estimates are well over a hundred years. So we're really not moving very fast. So we might actually, you know, I don't know, go to Mars before we have gender equity in physics. Who knows? And speaker-2: Is that a function of the community or is that a function of early learning and early sort of patterning of girls as opposed to boys? I mean, I know we're speaker-1: mean by is it a function of the community? speaker-2: Of the community, of the scientific community. So is it a function that the scientific community's just not embracing women in science or is it ha start ha is the phenomenon happened much earlier in life where girls just don't see themselves going into science, for example? speaker-1: It's not a man matter of embracing anybody. It's not like that's really the way we do science. It's not about one community embracing a group. It's that you're you're right at that of course the question of when kids decide that they want to do science or not, that's an important piece. What studies have shown is that there's no difference in the interest that girls versus boys have in physics or mathematics. In fact, now there's lots of studies that show that girls, of course Are performing better in math across the board and also in science, reading. That's not it. And there's other studies which show that high performing girls, let's say in high school, when they have to choose careers, the higher performing women students are less likely to choose science than lower performing men. So it's more than whether or not early childhood education has had an impact. This also, of course, plays a role. But Even when girls have had the opportunity to be, you know, go through the summer camps and lock the science and so on, they still don't choose careers. So there's more happening and that has to do absolutely with the environment. I think this is what you mean by the community embracing ⁓ women in science. It's about what's the environment for these women? Do they see themselves in a career in science? Is it about feeling that sense of belonging? These are questions that we have to ask. And there's again, this is ⁓ this is ongoing long time work. That shows that these are all factors. And absolutely, it has nothing to do really with the skill sets, which is really what has been the standard stereotype as ⁓ boys are better at maths, and you know, there's some kind of difference. Well, that's not the case, that's just an easy story. The real question is whether or not we are addressing all of the Stereotypes, the gender stereotypes, the resources, the the pieces that actually help us build successful careers in the STEM, such as funding. Are women being funded at the same level a as men? No. This is well, well documented. Are women acknowledged for their work at the same level? No. Are they given the same level of, you know, prizes and awards? No. Are they invited to speak about their work more? No. Do they have the same amount of time to do the work given that they usually take the larger proportion of childcare and elder care at home. These are all pieces. How fast are they be able to climb up the career ladder? This has also been documented. So it's not that we don't know what's going on. And these are nothing written in stone. It has nothing to do with our biologies. It has everything to do with our systems. So to me, on one hand it sounds like this is all negative, but I actually see it as a positive because none of this is written in stone. If it was really a biology question where there's a difference, that we can't change easily. But you know what we can change? We can absolutely change how we allocate resources. We can change whether or not women are supported when they have childcare and elder care. We can absolutely change whether we give equal awards and recognition to women. So to me these are all fixable problems. So let's fix the system as I keep saying and not look at fixing the women. speaker-0: Yeah. So can I can I add something? Yeah. I read a study about ten years ago that when I read it, it sort of metaphorically floored me. And it was a piece of work that was undertaken in order to understand gender bias and hiring practices. One of the experiments that the authors did, and by the way, it was gender bias and hiring practices in academia. One of the experiments that the authors did was they impaneled search committees from faculty at their institution and beyond. And they fed these search committees entirely fabricated personas. So they basically created a set of candidates who had proceeded to an interview stage. There were five candidates who proceeded to the interview. The search committee was given CVs and interview notes that were all sort of fictitious. And then the committees were asked to choose from these five candidates. And what they did was they kept the CVs they're all strong C Vs, all the interview notes were strong interview notes. And you could tell differences between them, but you would be hard pressed to actually choose between them necessarily because they they were all high quality. And and then what they did was they varied the number of women candidates in this. So five candidates, you can go from zero women to five women. So zero, one, two, three, four, five. And obviously if there's no women in the pool, the likelihood of picking a woman is zero. Right? And if everyone is a woman in the pool, then the likelihood of picking A woman is one hundred percent. The interesting thing was irrespective of if you had one woman, two women, three women, or four women, the likelihood of actually picking a woman candidate was only about twenty percent. And so it wasn't that if you if you could saturate your interview pools with women researchers, you were gonna be more likely to hire a woman. The the fact of the matter was if there was a single man, they were more likely to get. And the interesting thing was they did this for gender equity in research, but there was nothing in their data that suggested it would not apply to race, ethnicity, disability, anything. So I'll tell you where this actually went for me because I had spent by that point years trying to apply for faculty positions. And I can't tell you the number of faculty appointments that that I had and ⁓ the number of faculty applications that I made and the number of interviews that I had, the number of times I came second. But you don't win anything for coming second in in academia. You come second, that's nice. Who cares? Right? There's no medal for that. There's no funding for that. There's no startup funds. There's no half a lab somewhere for that. And the thought that I had was the only way, if I believe the premise of the research and and it was very strong research, and they validated this particular central experiment in three or four different ways, the only way that I, as a candidate with disabilities who's obvious about his lived experience because you can't Google me and not figure it out, is going to be as if the entire pool is full of candidates with disabilities. Then they have to pick one. Or they say funding's run out, we're not picking anybody. What does that end up meaning? It it ends up meaning that when we're talking about inclusion in STEM and we're talking about inclusion of different lived experience groups and different demographics in STEM, the system is actually self-replicating. So because the original system that existed decades ago was effectively all about white able-bodied men, that system of white able-bodied men replicates a system of white able-bodied men. So it sounds horribly cynical to say it, but it's also an intentional point, which is to some degree, it's not just about flooding the pipeline. Right? It's not just about saying there are more undergraduate students who are women who are Canadian white in the system in STEM today than ever before, or there are more graduate students in the system now who are not Canadian white, who are not Canadian white, who are persons who identify as living with disabilities, who are women who are gender minority. Like it's not about flooding the system. It's about recognizing that even when we flood the system, We still have to change the philosophies around who gets hired, right? And not just academia, but also all the STEM and STEM adjacent workspaces. speaker-1: Yeah, so I think it's not yeah, I I totally agree with you. In fact, I wish we could be, you know, living in in a world where it shouldn't only have to take other women being around to hire more women, right? As in women hiring women, men hiring men. In that's the not the model we want, right? It's that's not really inclusive or that's not being able to embrace those who are not like you. Right. So I feel like that's exactly what you're saying with the study that You have to be able to see people and their skill sets and their abilities beyond what it is that connects them to you, particularly through whatever demographic you belong to. So I'm not sure how we get there, but I would like to be in in a community where that would be possible. speaker-0: I think that requires us to do something that conventional wisdom says humans suck at, right? Humans are very bad at. I was in a meeting earlier today where we were doing something on psychological safety in the workplace, and the facilitator said an interesting thing, and that is humans think emotionally first, logically second. Now, if that's true, that also means that when we're thinking logically, we're thinking logically as filtered through an emotional lens. I mean, we can get into where biases come from and how they rear their heads and how they have to be sort of addressed and thought about and dealt with and is saying I'm unbiased really the way of doing that. But at the end of the day, if we're thinking emotionally first and logically second, but then we're fundamentally saying that what we want to do is we actually want to strip the emotion out of this conversation and and if you're thinking about hiring decisions, you're gonna hire the best candidate, right? But the best candidate may not look like you. The best candidate may not think like you, the best candidate may not drive the same model of car that you do, or frankly may not drive at all. And I I I still remember one faculty interview that I had somewhere south of the border where the city was notoriously bad for taxis. And despite having called a taxi an hour before the interview was supposed to start, I still showed up at the interview site only after the chair of the search committee came to get me because the taxi was late. Right. And so then the whole conversation for at least half the interview was how come you didn't drive a car and how do you get around if you don't drive a car? didn't get the position because we were talking about the science. We were talking about taxis. And I was sort of too inexperienced in the process and too inexperienced in kind of navigating or commandeering how a job interview is supposed to go to actually shove that interview back on frack. speaker-1: I mean honestly I'd love to b you know, again, thinking about that better future. I'd love to live in a world where it shouldn't be about whether or not we can, you know, remove ino emotions. Because I think honestly there's this huge sort of misconception that science is not an emotional advent endeavor and we're all objective. That's not how science works. I'm super passionate about I do happy dances when I get data and I you know, I get completely emotionally engaged with my work. I emotionally connect with others who are doing great work. That's how science gets done. It's absolutely about bringing our passion and our emotion. Of course, when we try to evaluate and assess what whether the science holds up, that's the part where we talk about objectivity and you know getting it. But we seem to confuse that with whether or not we can be at all emotional. So th I don't think we should take away emotions, but I wish we could, you know, emotionally connect with those who are doing science, regardless of whether they look like us or not. That's where we want to go, where we must be able to recognize emotionally and intellectually great scientists, regardless of their background, their demographics, where we fit in, how what they look like. Wouldn't it be great if we could be able to connect on that deep level, both emotionally and intellectually? Let's connect with those who ha have great ideas and can contribute or you can connect to them through the science. speaker-0: I mean, a good idea is a good idea. Exactly. The question becomes how we recognize good ideas even when they come out of s minds that don't think like ours and bodies that don't look like ours, right? And and fundamentally that's the premise behind doing science inclusively, right? I will tell you there was a research project that was undertaken during COVID nineteen that actually looked at the workplace experiences of people During the first phase, like the first lockdown and the pandemic. And it was a big nationwide survey. It was done by two faculty members at two comprehensive universities in Ontario. And I'm not going to name the universities. And in all the demographic questions that they listed, they didn't include the question, Are you a worker with a disability? And and I queried them on this and they punted it on to the research ethics office who basically said that was a personal health information question. You're not allowed to ask it. But they didn't push back on that. And they didn't push back on that. Conceptually, so to this massive nationwide survey, tens of thousands of responses, no disability data, couldn't use it for anything, right? That was disability related. So what ends up happening is if you have a perspective at the table where they have the ability to think laterally and to say, well, hang on a second, why not? Right? What's wrong with doing this? Why can't we do something this way? And so so you're wondering and you're asking the best question that scientists ask, which is why? But then we often get conditioned to ask questions and take default assumptions in certain ways because that's the way we grew up. That's how we learn. You know, the expression monkey see monkey do? Right? That's scientists. We're monkeys. And when we see what our previous generation of mentors did, that's what we're gonna do. So the the kind of scientist who is able to think apart from monkey see monkey do is the kind of scientist you'd think we would prize. But often what ends up happening is that element of thought process gets wrapped up in the This person doesn't think like me, this person doesn't look like me. In my time, I've seen some interestingly homogeneous labs and I've seen very diverse labs. And I've seen how those interestingly homogeneous labs preserve their homogeneity while the diverse labs sometimes will go through a fair bit of challenge, but often will attempt to preserve their diversity because they think it works better. And again, there are studies that actually show that it does work better. Yep. speaker-2: Well I did want to ask about learning and we can come at this from a couple of fronts, but specifically when is it important to learn about this, about the inc inclusion in science and how important that is? How early should that start and where should that speaker-0: All the time. Everywhere. speaker-1: Everywhere. speaker-0: ⁓ Well, I mean, the thing is that we learn about research methods in undergrad and we learn about research methods in grad school. But it's almost too late then. I'm gonna share a story and then Shahini, I'm I'm interested in your thought about it. You can ask any number of my students over the last ten years. I give very, very different interviews when I interview students. I interview students not for the skill sets that I know that they're trained to have. But rather for growth mindset and capacity to actually learn about disability and accessibility research. Right. There was one student I interviewed where it was quite clear that he had a very strong medical model mindset. And I said, I I didn't reject him out of hand. Like I knew he wasn't going to be a good fit for the team, but I didn't reject him out of hand. I actually said to him, if you are willing to expand the way that you think about the world. And you're willing to treat people with disabilities as human beings and not as objects to be fixed, then you'll have a great time working for me over the next four months. But if you can't do that, it's gonna be miserable for you. And I actually said, I said, I'm willing to take you if you're willing to learn. He called me a week later and said, I'm not willing to learn. He was honest. But the thing is that if you get to that point where you're in grad school and you're confronted by Principles of how to do inclusive science, because there are principles there are well researched, there's lots that's been written about them, and we can delve into that level of detail as well. But if you're confronted by these things and you're not willing to learn about them, then it's too late for you in certain spaces. So as early as possible and as often as possible. And I mean, fundamentally, I think the way to do that is somewhere along the line, we squash wonder out of children. Right. And we swash curiosity out of children. I once listened to a book talk given by Amanda Lang and she said something that stuck with me, which was basically we kids ask the best question. It's the question of why. And we beat it out of them by the age of five. And I'm mindful because I have a three and a half year old and if it happens to her, I will be very upset because I'm not the one who's doing it, and neither is her mother. But my perspective is that you need to retain A fundamental level of curiosity. And if you can retain that fundamental level of curiosity, you're always going to be asking, well, what about these people? And what about this scenario? And what about this thing that perhaps we haven't considered? Because when you're asking about why, you're asking about the edges that we don't think about. So in the early years, we teach inclusive science principles by nurturing the sense of curiosity and the sense of wonder. When we get into undergrad and grad school and college and research methods courses and things like that, then we can say, you know, that sense of curiosity that you've maintained all these years, this is how you do it. Right. But we can't start with that when someone's already twenty some odd years old. Yeah. speaker-1: So I totally agree with you, absolutely a hundred percent on the curiosity. In fact, I think that if you were to look around and look ask who are successful scientists today, yeah, of course there's all kinds of reasons why certain scientists are more successful than others and whether or not they've had all of the privileges of various things such as access to the right classes and the right resources and all of that. But One thing I think that is true, exactly going back to your point, Mahadeo, is that if they're successful scientists, most likely they've they are the ones who resisted that curiosity being beaten out of them. They're the ones who managed to maintain their curiosity. And plus they've been successful because of all kinds of other advantages, no doubt. But that curiosity is probably in there, which is why they continue. So I think maybe one thing I often face when I say, Well, inclusion is important and I'm trying to get people to learn about it on I face a lot of resistance, as I'm sure you do. Because scientists don't tend to think of that as a scientific skill, right? Inclusion. Right. When you connect it to curiosity, I think most successful scientists and scientists in general would agree that curiosity is a foundational skill to have. And I don't know if you want to call it a skill or not, but a foundational part of being a scientist. Yeah, exactly. So I feel like if we can that's one way to speaker-0: Yep, that's right. speaker-2: Almost like the competency actually. speaker-1: overcome this barrier, this resistance that folks have to thinking of inclusion as a foundational scientific competency. Because honestly, if you connect it to curiosity, which absolutely it is, all about that, then maybe we can make more progress to say, well, if you can somehow leverage the curiosity that you have for your work towards curiosity for the universe, which honestly includes people, you know, look around you. You may have missed that tiny point that The universe, ⁓ you know, we are scientists are humans. So we have to think about other humans if we want to be actually curious scientists. Let's examine everything. So I think it might be good to try to frame this whole question within this sort of scientific approach that folks like to think about, right? I don't know if that's necessarily the only way, but it seems to be one way to reach scientists who are more resistant to learning. speaker-0: So I'm I'm gonna say something that I think is potentially controversial. All research is curiosity-driven research. Even the applied research, even the thing where it's like, okay, I need to figure out how in order to understand a policy. So imagine, for example, that that we're thinking about aging in place policies, and we're thinking about how provincial and federal aging in place policies need to adapt to sort of changing incidents of lived experience with disability and changing demographics in Canada and so on and so forth. So you put a survey out there into the community of folks who are aging in place in order to understand what their experiences, their barriers, and their needs might be. And that survey is not accessible. All of a sudden, the very people, like a large subset of the very people that you're trying to get answers from, are not able to give you answers. You're you're going to get a subset of answers that comes from a subset of people. That is much more restricted than you perhaps would have wanted. And you're either gonna declare that limitation and hand in your report, in which case the policymaker is gonna say, I can't do anything with this, or you're gonna attempt to remove your limitation so that your report's more comprehensive and the policymaker is more likely to pay attention to it. Right. But it all of that comes from a space of being curious. So I would argue that all curiosity, all research All questions that are asked, all lines of scientific inquiry are curiosity driven by nature, whether they're applied or they're not applied. speaker-1: Addressing inclusion should be framed as a curiosity driven research problem. And that maybe people will actually be open to learning about it. speaker-0: Well, I mean, it it also needs to be not a theoretical research problem. People will look at this through that lens of sort of theoretical approaches. If this were something that we were to do, how would we do it? When in reality, you know, this is something we have to do. There's too many questions in the world where we don't think about them through the lens of how will everybody be affected? And they range from climate change to aging in place to artificial intelligence to quantum computing to all the different things that we can think of in all of the different flavors of scientific disciplines. And because we're not thinking about them through a curiosity driven inclusion perspective, then we're gonna limit ourselves on the kinds of answers we're gonna get. Yep. Well said. speaker-2: I think I just said we're wrapping up and it's been a good discussion and we could probably go deeper and deeper. We've we've certainly got to a great place. Maybe I'll just ask this question to you, Shaheene. What's the one takeaway that you would like researchers and scientists who might be listening to this podcast to have out of this podcast? What is the one thing that you want them to pay attention to if they are curious about this issue? speaker-1: Well, I think you said the word curious. So I would say maybe as a scientist, if you are perhaps you haven't thought about these issues deeply, you don't feel like you're ⁓ able to or you're equipped to, I would say the takeaway here is that you are already trained to address questions around inclusion because you are trained to be a curious person, you're trained to think about data, you're trained to ask about problems and think from a problem-solving perspective. You're trained to imagine how to improve a system. That's what science is all about, to be able to find new knowledge and apply it to do better and improve. And that's what inclusion is. So I think that could be at the takeaway for scientists. speaker-0: I think also when we think about it that way, we also come to an another fundamental conclusion, which is it's actually not that hard. But it is something ongoing. It is not a thing where you achieve a milestone and you're done. It's a thing where you constantly have to work at it. And as a result, there might be technical aspects of how to do this that you don't know answers to. But that doesn't mean that nobody knows answers. There's lots of us who know answers. Shonini knows answers. I know answers. And so speaker-1: But it is something ongoing. in research you often come across questions that you don't know the answers to and then you go and do some research or go and find the expert who can help you. speaker-0: That's right. I'll tell you one more story. I actually was asked to comment on a regulatory position at one point about something diabetes related. It was a diabetes related medical device that had received approval. And the way that it was worded, it was this is approved, but not for people who had diabetic retinopathy. And it it took a fair bit of sleuthing to figure out why. And it was because when Folks were doing ⁓ user experience testing on the device in question. The folks who were doing the testing didn't actually know how to construct a survey that would be accessible to somebody with vision loss. And so they said in their supplemental methods, because we didn't know how to construct a survey for somebody with vision loss, we didn't construct a survey for somebody with vision loss. Therefore, we excluded people with vision loss from actually doing this study, right? Yeah. Therefore, we don't know if this device is going to work for people with vision loss, right? Except how you construct a survey. To be taken by a person with vision loss is a solvable problem. And there are a number of people who know how to solve that problem. So this is actually where you've got curiosity, but you can also have failure of imagination. And so in this particular case, those researchers had a failure of imagination because what was to them an intractable problem actually had a solution. All they had to do was ask for it. And when you do something like that, that has implications, right? It has implications to How the work gets taken up. It has implications to how policies get made and how society ultimately interacts with what we're trying to do as scientists. And that's where I want to close by saying it's not that hard. There are people who have solutions that you may not have, but more than that, these are tractable things. And just because you don't know how to do it doesn't mean it can't be done. speaker-1: Great. So think about it like a research problem where you go away and do a literature review, find the folks who you want to collaborate with and get it done and keep improving. speaker-2: Thank Shaheeny, for joining us on our first podcast. And as you said at the upset, you can now include that in your bio. And maybe people will be curious about that. speaker-0: Thank you so much for coming to be with us, Shaheen. This is a great conversation. We'll have you speaker-1: Happy to be back any time. Thank you. speaker-2: Thanks everyone for listening to the Through the Idea Lens podcast. Join us next time when we talk with Bonnie Schmidt of Let's Talk Science, an organization dedicated to empowering the curiosity of children in STEM since nineteen ninety three. speaker-0: Thank you for joining us for this episode of Through the Idea Labs. The conversation speaker-1: Doesn't dent to you. speaker-0: Get social with us on YouTube, Instagram, TikTok, and Facebook at IdeaSTEM Canada. You can also learn more about our work at www.idea-stem.ca And you can email us directly at info at idea stem.ca Till next time, keep looking through the idea web. speaker-2: T. Until