This episode of the podcast wraps up 2021. I’ll return in January with all new episodes from the world of data visualization and presentation skills. I hope you have a healthy and happy new year!
Frank Elavsky is a software engineer turned researcher, embarking on a PhD at the intersection of accessibility and information-rich systems at Carnegie Mellon University. He is also the author of Chartability, a set of heuristics for evaluating the accessibility of data representations.
Episode Notes
Frank: Twitter | Observable | Patreon
Dataviz Accessibility Resources
Screen readers: NVDA | VoiceOver | JAWS
Tools: HighCharts | PowerBI | RStudio | Tableau
Related Episodes
Episode #201: Leland Wilkinson
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Transcript
Jon Schwabish: Welcome back to the PolicyViz podcast. I am your host, Jon Schwabish. And welcome to the final episode of the podcast for 2021, which to say the least, has been a challenging year. But I hope you will have some time. I hope you have the luxury of being able to spend this holiday season with friends and family and loved ones. And I hope you will stay healthy. And I hope you’ll be happy and be able to spend a little bit of time relaxing before we get into 2022, which I hope for all of us is a better year. And of course, I hope that we will get to see each other in person here or there or elsewhere.
Now on to the show, this final episode of 2021, I’m very excited to have Frank Elavsky join me on the program. Frank spends a lot of his time working in the area of data and data visualization accessibility. Now I spent a good chunk of my 2021 thinking about, talking about, writing about racial equity when it comes to data visualization, how do we talk about and to and represent the people and communities that we are focusing on or that we’re trying to communicate with. But one of the areas that we all need to be doing a better job with is how we make our content accessible to people who might have vision impairments, physical impairments, intellectual impairments. All the ways in which we might take for granted how we perceive and use information and use data is not necessarily the same experience for everybody.
So Frank has been doing a lot of research and a lot of writing, a lot of work in this area. And so we’re going to talk about in this episode pretty much the entire landscape of what the field of data visualization accessibility looks like and the work that he’s doing in his graduate program. There’s a lot of resources here that I’ve put in the show notes, especially linking back to the GitHub page that Frank co-hosts with a few others. And I hope you’ll check that out. And I hope you’ll think hard, especially as we go into the new year, about how you can make your data visualizations, your data products, everything that you communicate more accessible to more people, because the more we think about everybody, and how everybody can use our materials and our information, the better off we’ll all be and the better off we’ll be able to make our arguments and tell our stories. So, again, happy holidays. Happy New Year. I hope you enjoy this final episode of the PolicyViz podcast for 2021. And here’s my conversation with Frank.
Good morning, Frank. How are you? Good to see you.
Frank Elavsky: Good morning, John. I’m good. It’s good to see you too.
JS: Like great to see you, meet you virtually.
FE: Yeah.
JS: First time. Yeah, like, training emails and Twitter tweets are not like the same as like, actually, chatting.
FE: Yeah. This is a little different. It’s like level two. Level three would be in person. But–
FE: Right, right. Level three would be in person. Yeah. Maybe even without masks at some point, which would be, which would be very nice. So I’m excited to have you on the show. You’ve been doing a lot of work on issues in and around accessibility for people using data, reading data visualizations, and of course, creating those. And so I want to talk about all the things that you’ve been working on and the stuff that you’re doing now. So I thought maybe we would just start by, maybe you could just talk about the problem that you and a lot of you, you have a lot of collaborators all over the world. So like you in the — and your collaborators, and also people just sort of in those different areas, like what’s the problem you’re trying to solve? What are like the biggest challenges that you think content creators have when thinking about accessibility?
FE: Yeah, great. So there’s a big P problem, and a little P problem.
JS: Okay.
FE: And the little one I focus on specifically. But the big problem is, it’s obviously just inclusion, including people in, you know, what, what I think has been a pretty significant shift into the information age. So we’ve gone from not just having a lot of information, but now we’re starting to build products. We’re starting to use it. Information’s making its way, dense information is making its way into news articles, into personal applications, on our phones, on our computers all over the place. So it’s becoming ubiquitous, these data experiences that we have, and we’re using them to make decisions about our lives. And we also see this kind of rise of use in policy. Not that policy wasn’t data driven, but you know, it’s more and more so we see a lot of data being used to solve big world problems. So the big P issue here is including people in learning, in jobs, in decision making, in their own lives for the sake of the world. And, you know, 26% of Americans report having some kind of disability, right? And so that means, you know, there’s going to be a lot of inclusion efforts to kind of hit a broad spectrum of different kinds of people. You know, globally, low vision or uncorrected visual impairment affects almost 30% of people. It’s 28%. This one of the biggest global health inclusion efforts worldwide is vision more broadly. And so, you know, as you can guess, data visualization is, you know, it’s in a tricky situation. So that’s the big P problem.
JS: Yeah.
FE: The little P problem, I think you already kind of hit the nail on the head, which is, we’re producing this stuff at scale. And by we I mean practitioners, researchers, people who do this stuff and make this stuff. We’re producing things at scale. But we don’t really have the tools to make things accessible at scale. And it’s like we have a fire hose of, like, awesome ways to get data out there. I guess, sometimes, we may think our own workflows are too slow. And we’re always trying to improve how fast things were.
JS: Yeah. Right. Yeah.
FE: But you know, with Tableau, or, you know, like the rise, the advent of, you know, visually driven, like user interfaces for building like data experiences, it’s a lot easier than it was, and it’s getting even better. But we haven’t really made the same strides for making these things accessible. And there’s a huge gap that we’re beginning to form. And it’s only going to get worse if we don’t address it soon. And that gap is, you know, users and also practitioners with disabilities are being left out with this process.
JS: So I want to focus on vision for just a second because my feeling not data measured, but my feeling is that the data viz field focuses very heavily on color, what most people call color blindness, but color vision issues. So with that statement, I have two questions. First, perhaps more importantly, can you lay out some other vision difficulties or disabilities that people have that are not color, related to color? Because I get a feeling that maybe not a lot of people know, or familiar with what other vision challenges people might have. And then secondly, do you agree with that statement that like, far too much of the focus is on color and not on other forms of disabilities?
FE: Okay. Great question. This is spoken about in the past. So, you know–
JS: [unintelligible 00:07:16].
FE: Exactly. Thank you. Really set me up there.
JS: Yeah. Right. Yeah.
FE: So, yeah, you know, there’s layers to, I think, unlearning ableism. The work of making something accessible actually begins with an unlearning your own kind of ablest assumptions about what we need to fix or address in a given, in a given thing that we’re making. And, you know, visual impairment has a pretty broad spectrum. So when even just low vision, which is a way that we refer to kind of a large collection of things that are not just you don’t just need glasses, like low vision is like the next level. But it’s not blindness, which is also like kind of a category and blindness is also a spectrum, by the way, right? I think if they hear blindness, they think it’s just darkness. Actually, blindness is a spectrum of acuity. You know, one of the most common low vision impairments is caused by diabetes, diabetic retinopathy. And so this is like, you have like, large splotches of areas in your sight where you can’t see. And so actually, like you have decent acuity outside of that, but there are a lot of like issues in how you focus, how you move your eyes through content, etc, etc. So that’s just one example. And then, when it comes to your question about, you know, color vision deficiency, also known as color blindness, we actually have, like, a really rich history in research and practice dating back from like late 80s, early 90s about, you know, you know, how do we design and make tools for and research this particular issue, and it largely affects, you know, like, between 4 and 8% of, you know, XY chromosome individuals of European descent, and then it’s a much lower percentage, you know, from people of a different ancestry, and it’s about, you know, maybe 1% for people of an XX chromosome makeup, so, like, it largely affects men, and mostly white men, right?
JS: Yeah. Yeah.
FE: So you think about it, you know, I’m not gonna dig it too much to why this is another thing we’ve, you know, focused on a lot, and I actually don’t want to say that we shouldn’t focus on it because it’s still an inclusion effort. We still should. And, you know, we have a lot of great tools for making colorblind safe palettes, you know, for analyzing, you know, different types of color vision deficiency. we have like, great stimulators and all kinds of things. And actually, I think it’s a good example of, you know, CVD is a good example of, hey, there are a lot of other things we should have the same amount of tools and research for. Can we set this as kind of like a minimum goalpost for like a lot of other areas? I think that’d be great. So I don’t really want to like put it down, like, oh, we focused on it too much. But I do want to say like, let’s take this fervor that we’ve, you know, we’ve really worked hard to include people with CVD and data viz. Let’s just like keep doing that. Like, the quest doesn’t end there. Let’s keep going.
JS: Yeah, that’s great. So now you’ve helped built your own tool about accessibility.
FE: Yeah.
JS: Chartability. And so I just want to give you a chance to just like, I was gonna say, like, just talk about it. But actually, maybe I’ll help structure that the question a little bit. So talking about accessibility tools, and writing about them sort of blog posts, and books, and what have you in articles is one thing, but actually building a tool, a functional tool that people can use is a totally different thing. So can you talk a little bit about where that originated, and then how you built it? And then, and then for folks who don’t know about it, and of course, put the link in the show notes. But for folks who don’t know about it, you know, what it is, and what it helps them do?
FE: Yeah. Okay. Great. So let’s see if I can do that in order. Sorry. What’s the very first thing you asked?
JS: Okay. So first one was origins.
FE: Right.
JS: And then and then sort of how the, how that process of building it went?
FE: Okay. Yeah. So if we think back to my little P problem, that people are not able to address accessibility at the same scale that we are able to produce experiences, right, driven by data, like we’re making way more data viz than we can, than we’re making them accessible.
JS: Right.
FE: I think that, to me, the first step is people just need to be able to evaluate whether or not something is or is not accessible according to a robust set of criteria, right? That’s step one for me. So that is where chartability comes in. That’s kind of like the, the origin for it. It’s a synthesis of quite a lot of things. Took WCAG, which is Web Content Accessibility Guidelines. It’s a global standard. It affects like 56% of the world’s population in some form of governance or policy. So like, it’s the big standard for technology that we have. A chocolate cake standards, which are pretty, like broad in their application, and focus them specifically on what I call data experiences. But it’s mostly data visualizations, or data driven interfaces. So I took that set of standards, made them relevant, technically speaking, for, you know, a specific line of work. And then I also brought in research. There’s a lot of great research in the data viz space, including related to accessibility that just has never made it to global standards for, you know, probably because it’s so specific, I’m assuming, but global standards also move slow. Don’t hate me, WCAG people. I love you all. But you know, it’s, it’s very slow moving process.
JS: Yeah.
FE: And so I’ve also brought that stuff in and synthesized it. So at its base, it’s a set of heuristics. It’s a set of tests, right? Like, test this thing, do this thing. And, and at the heart of it, that’s, that’s what it is. That’s its origin story.
JS: And so for those who haven’t gone to the chartability site, how do you hope that they will use the tool?
FE: Yeah. It takes bravery. Because you’re going to be embarking into uncharted space, when you start using it. It’s a much, much, much lower learning curve than, like international accessibility standards, like, WCAG. WCAG is very hard to start getting into to start learning. So it’s easier there. But it’s still difficult. You’re going to have to practice using a screen reader. That’s just one of the 50 tests in chartability. The current release has 45, but the next release will have 50. So it’s like, you’re going to have to use a tool you’ve never used before. You’re going to have to kind of like learn how to do a contrast ratio test. Like, you know, these are things people haven’t really, you know, a lot of people haven’t really done. So it’ll take some, you know, guts to give it a shot. Your first time doing it might take you like, if you do the whole thing, it could take you like four hours.
JS: Yeah.
FE: You know, it’ll take you a while.
JS: But is it, but from your perspective, is it kind of like getting the machines up and running? Like once you sort of get the machines going, then it’s four hours the first time, but the next time it’s gonna take you, I mean, fraction of that time?
FE: Oh, yeah. I can, I can just tell by looking at things most of the time now. And because I’m so like, I’ve seen so many of the same issues. I usually know like, right away like the three or four things I should just check first and, you know, good chance, there’s gonna be some failures there. So yeah, it’s supposed to help people audit. And one of the things I think that people don’t realize about auditing, is that a good audit? If you measure an audit in terms of its effectiveness, it caught failures, right?
JS: Yeah.
FE: So you’re not supposed to use it to kind of like say, “Oh, I did a good job. I only had 10 things wrong.” No, no, no. You put on your auditor hat. You take off your data viz person hat. And you say like, okay, my job right now is to see how many things I can catch. And that’s it. That’s your only goal is just to catch things that were not accessible or inaccessible about your work.
JS: Right.
FE: So, yeah.
JS: Can you talk real briefly about screen readers for maybe for folks who don’t know how they work? In particular, I mean, we could talk about the basics of how it, you know, reads a document out loud, but with data viz in particular, because that’s, that’s our focus. Like, I don’t really know what my question is here, to be honest. But like, maybe like, you know, do you have a particular screen reader that you like to use, especially for testing? And it’s, you know, I find some of them are kind of hard to like the, even the built in one on the Mac is sort of hard to get up and running.
FE: Yeah.
JS: It should just be like a click of a button, like, on my Xbox, you literally click a button, and you turn on the accessibility features and it reads it aloud. Like I don’t know why it’s so hard on the Mac. But anyways, and then, like, are there tools out there? And we don’t need to, like, you know, hammer people, but like other ones that you think are good? Or let me, that’s it. Let me say not say good, better at some of these tasks.
FE: Okay. All right.
JS: I keep giving you like 400 questions in one time. But I’ve got to watch. I want to learn from you, Frank. Just like, just give, just feed me all the information.
FE: All right. So screen readers. My favorite is NVDA for a few reasons. One, it’s free. Two, it’s community contributed. So like, there’s a lot of advantages and disadvantages to that model, right? But the fact that it’s free is great and only is for PCs.
JS: Yeah.
FE: So, yeah, that’s a disadvantage. If I’m only going to test like minimally, I do on the PC NVDA and then on a Mac VoiceOver. Those two are like cover a pretty broad spectrum. But really, like JAWS is the most used screen reader. So if anybody does this work professionally, you should like get a license for your org. It’s like 1,000 bucks or so. And because it’s, it’s actually the one that most people use, it’s still the most popular. So, yeah, VoiceOver is really intelligent. And it’s so good at certain things that you have to test stuff with it because it will have a different experience than other screen readers.
JS: Oh, because you’re saying.
FE: Yeah. Folks at Apple, I mean, I worked with some of them. So I’m not trying to butter them up. But like, CMU has a lot of like Apple connections in ATI. So like, yeah, voiceovers pretty smart. And I don’t want to say that other screen readers aren’t, but they’re like, they very much follow the API for assistive technologies of their class. So they’re just trying to just, you know, fit that standard. And so you’re going to get a little different experience across all of them, honestly, and in different browsers and whatever.
JS: So right, but JAWS, but you’d say JAWS is the, what, sort of the enterprise level, professional level screen reader. Okay.
FE: Yeah. It’s the Pro Tool. It’s like, yeah, it’s the oldest. It’s the most established. It’s also the most used.
JS: Right.
FE: Yeah. It’s, it’s, I mean, I don’t want to say it’s the best because they all have pros and cons. But yeah.
JS: Yeah. Okay. Great.
FE: Yeah. And, you know, for folks who aren’t familiar with like, what’s like, why screen readers? There’s, like, there’s a whole world on this. But I think Leonie Watson, a good friend of mine, she’s a accessibility subject matter expert, but also, you know, dabbles in data visualization. She’s a native screen reader user. And that’s how we refer to somebody who, you know, uses it in part of their daily life. And she wrote an article on voice modulation and synthetic speech, and basically explaining why screen readers still use this kind of like robotic voice. And it’s because screen reader users are very fast. Like you first turn on a screen reader, it reads super slow. It’s like if you watched all your videos on YouTube at quarter speed, like it’s just painfully slow for somebody who uses this tool daily. And you know, like experts, screen reader users will listen at a speed of like 400 words per minute, which is way faster than we would ever talk.
JS: Yeah, wow.
FE: But it’s because it’s how they get their information, and they’re very used to it and very efficient. And so then the main advantage of the screen reader is it’s like its speed, its efficiency. Yeah, they’re pretty well established standards in certain environments also. So, yeah.
JS: Wow. Yeah. So then what about on the, on the data viz tools? Have you found that some tools are better at accessibility? I mean, I know the folks over at Power BI, who spent a lot of time thinking about accessibility, and from what I’ve heard, like, Tableau may not be as sort of, I don’t want to say bad. Again, I don’t want to say as advanced.
FE: If you wear their true, yeah, if you wear our chartability hat, they’re all bad because they all had failures.
JS: Yeah, yeah, right, okay. So which one then has the fewest, like, audit checkmarks if you go through chartability?
FE: It’s, it’s very tough, because as Chris DeMartini has shown in his like Tableau accessibility journey, you can hack Tableau to do like pretty wild things, like have keyboard navigation on Chart Elements. That’s something that Power BI has, the Tableau does not. But you can hack Tableau to do it, right. So if we just compare the tool, it’s like, okay, you could, you could technically pass if you spend like 40 hours on this one issue, and you’re a Tableau Zen master, right? So like, so like, a real thing that I measure is not that you can, like, create a perfect test case that does really good because a lot of these tools actually can be pretty competitive in that if you set it up like that, right?
JS: Yeah.
FE: But it’s like can an everyday data viz person kind of put something together that like really works? And yeah, actually, I mean, I don’t want to say Power BI is good, because, like, there are so many things, you know, that they know me. They all know me. So like, yeah, there are a lot of things, I think that they could improve on, they are improving on. But thinking in terms of labor is a very good way to frame accessibility. Like you want to pick a tool that makes the least amount of work for you, the practitioner, and also the least amount of work for your user. You know, Chris DeMartini also in his accessibility journey with Tableau measured how many keystrokes he had to press when using a screen reader to get the information he needed out of a visualization. And it was like 130 or something. 130 keystrokes to just get, like, basic info, and the cognitive load was so heavy, because it’s like, how can you even store this information in your head, as you’re going through it? There’s so much, but when he added like, a keyboard navigable thing, and the data is like right there, right in the visual. You’re like, just going through it, he was immediately able to jump in and start getting, you know, information much, much faster.
JS: Right, right, interesting.
FE: And so, yeah. There’s like all kinds of tradeoffs. I will say, though, the absolute best stuff is like, you know, you’re coding at a low level in JavaScript. Like, that’s the best stuff. So we’re talking like Highcharts, Visa Chart Components. I’m biased, because that’s the library I contributed to as at least that, but yeah, there’s some really good ones out there that I think Highcharts is definitely the one that it’s like, they set the standard for everyone else. They have sonification. They have like tools to export for tactile graphics. I mean, they’re like, they’re really kicking butt. They really care about it.
JS: That’s great. That’s great. That’s good to know. Okay. So I’ll list all these in the show notes for people, you know, Chris’s work and links to some of these other articles that you’ve mentioned. So, before I let you go, I want to talk about your, what you’re doing right now. So you’re back in grad school. So I’m excited to hear about the program. Once you get through this first year, the dreaded first year of grad school, then you get to do the fun part, which is the research. So like, I’m sure you have like this big binder of things that you want to write about, but like, yeah, tell folks about like the whole, the whole situation.
FE: Okay. So I don’t know what it’s like elsewhere. But I’m expected to do research right now.
JS: Oh, right away, okay.
FE: It’s going.
JS: Econ, you just like do math. There’s like, I used to call it math with no numbers. You just do math theory for like the first year.
FE: Oh, spicy.
JS: Then you could take exams, and then you could start, and then you could finally start [unintelligible 00:24:06].
FE: Well, honestly, that would be nice to have a year just to kind of–
JS: Learn.
FE: Compare.
JS: Yeah.
FE: But no, I would go on. We’re going already.
JS: All right.
FE: Yeah. So, yeah. I, obviously, I’m hoping to focus on what I’m calling information rich systems. And I’m doing this. I’m not specifically talking about data visualization, because I want to also anticipate future interfaces, future ways of working with data. And really the core problem to me is the richness and density and complexity that we use information and how do we make kind of that stuff accessible? Data visualization is, obviously, to me the first step. And accessibility then, like, what in accessibility, this broad, broad world, what am I going to focus on? And obviously, I said, my big, my little P problem is, you know, helping practitioners do stuff. So I’m going to try and, you know, really focus kind of on a computer sciences end of things, making tools that help people do this work. But also, I’m really interested in motor impairment and animation. There’s a lot of things that really have been under studied and under focused in, in terms of accessibility. You know, motor impairment is like, you know, data visualization still largely lives in a mouse point, click, drag kind of paradigm. And not only is that not even suited to the times we live in, but also it’s not very accessible.
JS: Yeah.
FE: And so I’m kind of curious, you know, what are other interaction modalities that we can look at? And how do we adapt a system to suit the user’s needs. So if I can do one thing that’s kind of academic, it’s just to emphasize, when you think about accessibility, try not to expect the user to adapt to the system, try not to augment the user so that they fit the system’s expectations. And instead, try and design your system to have like a breadth of a robust set of adaptations that can suit different user input. Just mouse input is gonna have a lot of what’s called ability assumptions about your users. But if you have keyboard accessibility that actually suits a lot of assistive technologies, a lot of AT use the keyboard interface. So you’re going to immediately make your visualization a lot more accessible to a lot of other people just by keyboard, and then touch, and then other inputs also, you can really explore. But I would say at least keyboard and mouse and touch are like kind of the Holy Trinity.
JS: Yeah.
FE: But yeah, there’s also a lot of other ways to think about adapting your system to suit user needs that I think don’t just apply to accessibility. It’s just, you know, I’ve talked about this a lot too. Data visualization has this problem, where we kind of design stuff with the assumption that it’s static, but we don’t live in a static world anymore. We have these digital tools. Why not allow users to self advocate to set preferences to even adjust the visualization space itself to suit their needs?
JS: Right.
FE: And, and I think that there’s a lot of room for that. You know, I would say data driven journalism explores this more than anywhere else.
JS: Yeah.
FE: At scale. I think they do a really good job at this. But I think that, you know, the business space, building analytical tools, there’s a huge potential there as well also.
JS: Yeah, and if there’s, you know, 26% of Americans reporting some type of disability, I mean, that’s a huge, that’s a huge market, right.
FE: Yeah. I mean–
JS: That’s going untapped, right? I mean, yeah.
FE: Yeah. Yeah. Yeah. I heard somebody talk about how they, they proposed a model to Google or something. And it was like only 93% effective. And Google was like, I don’t care. Get better. You need to get much, much higher, before we’re even going to be interested in you. And then I’m thinking, okay, data visualization at most, you know, especially like these complex, but flimsy things that we build, they’re very, they’re very fragile experiences, right?
JS: Right.
FE: They’re at most 75% effective, right? And so, okay. How’s that? You know, we got to really improve our, you know, market and effectiveness here. So, yeah.
JS: Absolutely. I want to let you go. But you mentioned motor impairments. And so I wanted to ask one last question, because one of the things that I find in talking about accessibility is that they’re sort of limited. And this goes back to our earlier conversation with color, sort of a limited, somewhat limited perspective on what accessibility means, right? There’s sort of like a big focus on color. And that might be because especially for people just starting out, it’s easy to say, hey, you know, like you said, there’s a lot of ton of tools. So you could do a quick test, and you can sort of fix that. But there’s motor impairments. There’s, there’s physical impairments. There’s intellectual impairments. But I also wanted to ask you specifically about access, just, just access to a lot of these tools. Like, we make a lot of things that are like require a lot of bandwidth. But like, we know that not everybody has access just generally to like a good, like broadband access. So I guess my question is, I don’t know what my question is. I guess, just like the, just like the broader thought about accessibility, just like okay, so maybe I’ll try to crystallize this into a question. As you can see, when I do these podcasts, it’s just kind of like off the cuff, right. And you’ve alluded to this already many times. But like, what’s the thing that people should keep in mind when they think about accessibility in their work?
FE: Yeah. It’s definitely, and this is why data visualization is a beautiful place for doing accessibility. And I think there’s a rich potential to like just get tons of practitioners excited about accessibility is because accessibility is about thinking about your audience. It’s about recognizing, is this experience painful? Is it difficult for some people? And then making it better. If you think about it, the heart of visualization is looking at data and saying this is terrible. This is bad. Let’s, let’s see if we can visualize this and make it easier, right?
JS: Right.
FE: So like we’re already doing the core activity of providing assistance, right, is, is what we do with visualization. And like, let’s keep going. It doesn’t stop there. And so, yeah, it’s, it’s about what are called Ability assumptions. That’s like, the thing you should think about. What are the assumptions I have about my audience’s ability? And how can I reach more people? And ability, I’m glad that you talked about bandwidth. Because data visualization is actually a really great way to transfer. We’re talking actual, like bytes of data in a small package, because images could be much smaller than entire databases. Right?
JS: Right.
FE: But data visualization has a tendency to actually really bloat the space, right? So that you have like really high graphics requirements, connection requirements, etc. And there’s a global access issue that I think a lot of people are really not considering. I do know some folks that are I would say that 538, the folks I’ve talked to there, this is like one of their like core things they’re focusing on. I think it’s beautiful. And so yeah, just really trying to be as inclusive as possible in your work by questioning your ability assumptions, it’s undoing ableism, right. We’re all this way. We all have internal ableism. It’s not like it’s bad to say that you do. I do. And it’s just part of the work. It’s just undoing your assumptions.
JS: Yeah. That’s great. Frank, thanks so much for coming on the show. I mean, I have learned a ton. I’ve got a lot of more reading to do. Thanks so much. Good luck in grad school. I’m excited to see all the stuff that comes out. And yeah, thanks again for coming on the show.
FE: Great. Thank you so much.
JS: And thanks, everyone, for tuning in to this week’s episode of the show, and all the shows in 2021. Hopefully, you’ll get a little bit of a break, maybe go back and check out a few of the great episodes that we’ve put up over this past year. And of course, I would be remiss if I didn’t thank all the people who helped me with the show. Of course, all the guests who have come on the show, taking time out of their schedule to answer my questions and to put up with me is I veer off the questions that I said I would ask them and to ask other questions. Big thank you to the folks who helped me with the transcription. Big thank you to Ken Skaggs for helping with the audio editing. And a big thank you to Sharon Sotsky Remirez for all the editing and the advertising and all the things that go together in making this podcast a success. If you would like to help see the podcast continue into 2022, please consider making a financial contribution over at Patreon or on my Pay Pal channel, or just spread the word. Put a review up on iTunes, Spotify, or your favorite podcast provider. Whatever it is, have a healthy happy holidays, and a happy and healthy New Year. And I will talk to you in 2022. So until next time, this has been the PolicyViz podcast. Thanks so much for listening.
